Blog Posts for the ‘Time’ Category

Project Estimation and Black Swans (Part 3)

Wednesday, October 20th, 2010

Last time out, we determined that mucking with the estimate to account for variance and surprises in projects is in several ways wanting. This time, we’ll make some choices about the tasks and the projects, and see where those choices might take us.

Leave Problem Tasks Incomplete; Accept Missing Features

There are a couple of variations on this strategy. The first is to Blow The Whistle At 100. That is, we could time-box the entire project, which in the examples above would mean stopping the project after 100 hours of work no matter where we were. That might seem a little abrupt, but we would be done after 100 hours.

To reduce the surprise level and make things a tiny bit more predictable, we could Drop Scope As You Go. That is, if we were to find out at some point that you’re behind our intended schedule, we could refine the charter of the project to meet the schedule by immediately revising the estimate and dropping scope commitments equivalent to the amount of time we’ve lost. Moreover, we could choose which tasks to drop, preferring to drop those that were interdependent with other tasks.

In our Monte Carlo model, project scope is represented by the number of tasks that we attempt. After a Wasted Morning, we drop any future commitment to at least three tasks; after a Lost Day, we drop seven; and after a Black Cygnet, we drop 15. We don’t have to drop the tasks completely; if we get close to 100 hours and find out that we have plenty of time left over due to a number of Stunning Successes, we can resume work on one or more of the dropped tasks.

Of course, any tasks that we’ve dropped might have turned out to be Stunning Successes, but in this model, we assume that we can’t know that in advance; otherwise, there’d be no need to estimate. In this scenario, it would also be wise to allocate some task time to manage the dropping and picking up of tasks.

I’ve been a program manager for a company that used a combination of Blow The Whistle and Drop Scope As You Go very successfully. This strategy often works reasonably well for commercial software. In general, you have to release an update periodically to keep the stock market analysts and shareholders happy. Releasing something less ambitious than you hoped is disappointing. Still, it’s usually more palatable than shipping late and missing out on revenue for a given quarter. If you can keep the marketers, salespeople, and gossip focused on things that you’ve actually done, no one outside the company has to know how much you really intended to do. There’s an advantage here, too, for future planning: uncompleted tasks for this project represent elements of the task list for the next project.

Leave Problem Tasks Incomplete; Accept Missing Features AND Bugs

We could time-box our tasks, lower our standard of quality, and stop working on a task as soon as it extends beyond a Little Slip. This typically means bugs or other problems in tasks that would otherwise have been Wasted Mornings, Lost Days, or Black Cygnets, and it means at least a few dropped tasks too (since even a Little Slip costs us a Regular Task).

This is The Perpetual Beta Strategy, in which we adjust our quality standards such that we can declare a result a draft or a beta at the predicted completion time. The Perpetual Beta Strategy assumes that our customers explicitly or implicitly consent to accepting something on the estimated date, and are willing to sacrifice features, live with problems, wait for completion of the original task list, or some combination of all of these. That’s not crazy. In fact, many organizations work this way. Some have got very wealthy doing it.

Either of these two strategies would work less well the more our tasks had dependencies upon each other. So, a related strategy would be to…

De-Linearize and Decouple Tasks

We’re especially at risk of project delays when tasks are interdependent, and when we’re unable to switch the sequence of tasks quickly and easily. My little Monte Carlo exercises are agnostic about task dependencies. As idealized models, they’re founded on the notion that a problem in one area wouldn’t affect the workings in any other area, and that a delay in one task wouldn’t have an impact on any other tasks, only on the project overall. On the one hand, the simulations just march straight through the tasks in each project equentially, as though each task were dependent on the last. On the other hand, each task is assigned a time at random.

In real life, things don’t work this way. Much of the time, we have options to re-organize and re-prioritize tasks, such that when a Black Cygnet task comes along, we may be able to ignore it and pick some other task. That works when we’re ultimately flexible, and when tasks aren’t dependent on other tasks.

And yet at some point, in any project and any estimation effort there’s going to be a set of tasks that are on a critical path. I’ve never seen a project organized such no task was dependent on any other task. The model still has some resonance, even if we don’t take it literally.

A key factor here would seem to be preventing problems, and dealing with potential problems at the first available opportunity.

Detect and Manage The Problems

What could we do to prevent, detect, and manage problems?

We could apply Agile practices like promiscuous pairing (that is, making sure that every team member regularly pairs with every other team member). Such things might to help with the critical path issue. If each person has at least passing familiarity with the whole project, each is more likely to be able to work on a new task while their current one is blocked. Similarly, when one person is blocked, others can help by picking up on that person’s tasks, or by helping to remove the block.

We could perform some kind of corrective action as soon as we have any information to suggest that a given task might not be completed on time. That suggests shortening feedback loops by constant checking and testing, checking in on tasks in progress, and resolving problems as early as possible, instead of allowing tasks to slip into potentially disastrous delays. By that measure, a short daily standup is better than a long weekly status meeting; pairing, co-location and continuous conversation are better still. Waiting to check or test the project until we have an integration- or system-level build provided relatively slow feedback for low-level problems; low-level unit checks reveal information relatively quickly and easy.

We could manage both tasks and projects to emphasize information gathering and analysis. Look at the nature of the slippages; maybe there’s a pattern to Black Cygnets, Lost Days, or Wasted Mornings. Is a certain aspect of the project consistently problematic? Does the sequencing of the project make it more vulnerable to slips? Are experiments or uncertain tasks allocated the task time that they need to inform better estimation? Is some person or group consistently involved in delays, such that training, supervision, pairing, or reassignment might help?

Note that obtaining feedback takes some time. Meetings can take task-level units of times, and continuous conversation may slow down tasks. As a result, we might have to change some of our tasks or some part of them from work to examining work or talking about work; and it’s likely some Stunning Successes will turn into Regular Tasks. That’s the downside. The upside is that we’ll probably prevent some Little Slips, Wasted Mornings, Lost Days and Black Cygnets, and turn them into Regular Tasks or Stunning Successes.

We could try to reduce various kinds of inefficiencies associated with certain highly repetitive tasks. Lots of organizations try to do this by bringing in continuous building and integration, or by automating the checking that they do for each new build. But be aware that the process of automating those checks involves lots of tasks that are themselves subject to the same kind of estimation problems that the rest of your project must endure.

So, if we were to manage the project, respond quickly to potentially out-of-control tasks, and moderate the variances using some of the ideas above, how would we model that in a Monte Carlo simulation? If we’re checking in frequently, we might not be able to get as much done in a single task, so let’s turn the Stunning Successes (50% of the estimated task time) into Modest Successes (75% of the estimated task time). Inevitably we’ll underestimate some tasks and overestimate others, so let’s say on average, out of 100 tasks, 50 come in 25% early, 49 come in 25% late. Bad luck of some kind happens to everyone at some point, so let’s say there’s still a chance of one Black Cygnet per project.

Number of tasks Type of task Duration Total (hours)
50 Modest Success .75 37.5
49 Tiny Slip 1.25 61.25
1 Black Cygnet 16 16

Once again, I ran 5000 simulated projects.

Average Project 114.67
Minimum Length 92.0
Maximum Length 204.25
On time or early 1058 (21.2%)
Late 3942(78.8%)
Late by 50% or more 96 (1.9%)
Late by 100% or more 1 (0.02%)
Image:  Managed Project

Remember that in the first example above, half our tasks were early by 50%. Here, half our tasks are early by only 25%, but things overall look better. We’ve doubled the number of on-time projects, and our average project length is down to 114% from 124%. Catching problems before they turn into Wasted Mornings or Lost Days makes an impressive difference.

Detect and Manage The Problems, Plus Short Iterations

The more tasks in a project, the greater the chance that we’ll be whacked with a random Black Cygnet. So, we could choose your projects and refrain from attempting big ones. This is essentially the idea behind agile development’s focus on a rapid series of short iterations, rather than on a single monolithic project. Breaking a big project up into sprints offers you the opportunity to do the project-level equivalent of frequent check-ins in on our tasks.

When I modeled an agile project with a Monte Carlo simulation, I was astonished by what happened.

For the task/duration breakdown, I took the same approach as just above:

Number of tasks Type of task Duration Total (hours)
50 Modest Success .75 37.5
49 Tiny Slip 1.25 61.25
1 Black Cygnet 16 16

I changed the project size to 20 tasks. Then, to compensate for the fact that the projects were only 20 tasks long, instead of 100, I ran 25000 simulated projects.

Average Project 22.94
Minimum Length 16
Maximum Length 66.75
On time or early 12433 (49.7%)
Late 12567 (50.3%)
Late by 50% or more 4552 (18.2%)
Late by 100% or more 400 (1.6%)
Image: Agile Project

A few points of interest. At last, we’re estimating to the point where almost half our the projects are on time! In addition, more than 80% of the projects (20443 out of 25000, in my run) are within 15% of the estimate time—and since the entire project is only 20 hours, these projects run over by only three hours. That affords quick course correction; in the 100-hours-per-project model, the average project is late by three days.

Here’s one extra fascinating result: the total time taken for these 25000 projects (500,000 tasks in all) was 573,410 hours. For the original model (the one above, the first in yesterday’s post), the total was 619,156.5 hours, or 8% more. For the more realistic second example, the total was 736,199.2 hours, or 28% more. In these models, shorter iterations give less opportunity for random events to affect a given project.

So, what does all this mean? What can we learn? Let’s review some ideas on that next time.

Project Estimation and Black Swans (Part 2)

Sunday, October 17th, 2010

In the last post, I talked about the asymmetry of unexpected events and the attendant problems with estimation. Today we’re going to look at some possible workarounds for the problems. Testers often start by questioning the validity of models, so let’s start there.

The linear model that I’ve proposed doesn’t match reality in several ways, and so far I haven’t been very explicit about them. Here are just a few of the problems with the model.

  • The model tacitly assumes that all tasks have to be done in a specific order.
  • The model tacitly assumes that all tasks are of equal significance.
  • The model leaves out all notions of tasks being independent or interdependent with respect to each other.
  • The model assumes that once we’re into a Wasted Morning, a Lost Day, or a Black Cygnet, there’s nothing we can do about it, and that we won’t do anything about it.

In particular, the model leaves out control actions that could be applied by managers or by the people performing the tasks, control actions that could be applied to the tasks, the project, the context, or to the estimates. Let’s start with the latter.

Pad The Estimates So We’re Half Right

Here’s the chart of yesterday’s first scenario again:

Under the given set of assumptions, and assuming random distribution, we come in late a little over 90% of the time. To counter this, we could add some arbitrary percentage to our estimates such that half the time we’ll come in early, while the other half of the time we’ll (still) come in late. In that case, we’d want to pick a median value.

When I used the data from the Monte Carlo simulation and sorted the project lengths, I found that Project 2500, the one right in the middle, has a length of 122 hours. So: pad the estimate by 22%, and we’ll be on time 50% of the time.

There are two problems with this. The first is that there’s still significant variability in terms of how late.  Second, the asymmetry problem is the same for projects as it is for individual tasks: our big losses have a greater magnitude than our big wins. Even if we go for the average project length, rather than the median (the average 123.83 hours, is a couple of hours longer), fewer projects will go over the estimated time, but early projects will tend to be more modestly early, while the late ones will be more extremely late. None of this is likely to be acceptable to someone who values predictability (that is, the person who is asking us for the estimate).

Pad The Estimates So We’re Almost Always Right

Someone who likes predictability would probably prefer our projects to come in on time 95% of the time. If we wanted to satisfy that, based on the same set of assumptions, we would do the best estimating job we could, then pad our estimate by 58%, to 158 hours.

One problem with that strategy is that work tends to expand to fill time available, and people will start to work at a slower pace.

One the other hand, if people keep the regular pace up, 82% of our projects are going to come in at least 10% early, and 42% of our projects will come in 25% early! In such a case, we’ll probably face political backlash and be urged to less conservative with our estimates. By the math, we really can’t win under this set of assumptions.

Pad The Team

Rather than padding the estimate of time, we could build slack into the system by having extra people available to take on any surprises or misunderstandings. But note Fred Brooks’ Law, which says that adding people to a late project makes it later. That’s because of at least two problems: the new people need to be brought up to speed, and having more connections in a system tends increases the communication burden.

So maybe we’ll have to change something about the way we manage the project. We’ll look at that next.

Project Estimation and Black Swans (Part 1)

Thursday, October 14th, 2010

There has been a flurry of discussion about estimation on the net in the last few months.

All this reminded me to post the results of some number-crunching experiments that I started to do back in November 2009, based on a thought experiment by James Bach. That work coincided with the writing of a Swan Song, a Better Software column in which I discussed The Black Swan, by Nassim Nicholas Taleb.

A Black Swan is an improbable and unexpected event that has three characteristics. First, it takes us completely by surprise, typically because it’s outside of our models. Taleb says, “Models and constructions, those intellectual maps of reality, are not always wrong; they are wrong only in some specific applications. The difficulty is that a) you do not know beforehand (only after the fact) where the map will be wrong, and b) the mistakes can lead to severe consequences. These models are like potentially helpful medicines that carry random but very severe side effects.”

Second, a Black Swan has a disproportionately large impact. Many rare and surprising events happen that aren’t such a big deal. Black Swans can destroy wealth, property, or careers—or create them. A Black Swan can be a positive event, even though we tend not to think of them as such.

Third, after a Black Swan, people have a tendency to say that they saw it coming. They make this claim after the event because of a pair of inter-related cognitive biases. Taleb calls the first epistemic arrogance, an inflated sense of knowing what we know. The second is the narrative fallacy, our tendency to bend a story to fit with our perception of what we know, without validating the links between cause and effect. It’s easy to say that we know the important factors of the story when we already know the ending. The First World War was a Black Swan; September 11, 2001 was a Black Swan; the earthquake in Haiti, the volcano in Iceland, and the Deepwater Horizon oil spill in the Gulf of Mexico were all Black Swans. (The latter was a white swan, but it’s now coated in oil, which is the kind of joke that atracygnologists like to make). The rise of Google’s stock price after it went public was a Black Swan too. (You’ll probably meet people who claim that they knew in advance that Google’s stock price would explode. If that were true, they would have bought stock then, and they’d be rich. If they’re not rich, it’s evidence of the narrative fallacy in action.)

I think one reason that projects don’t meet their estimates is that we don’t naturally consider the impact of the Black Swan. James introduced me to a thought experiment that illustrates some interesting problems with estimation.

Imagine that you have a project, and that, for estimation’s sake, you broke it down into really fine-grained detail. The entire project decomposes into one hundred tasks, such that you figured that each task would take one hour. That means that your project should take 100 hours.

Suppose also that you estimated extremely conservatively, such that half of the tasks (that is, 50) were accomplished in half an hour, instead of an hour. Let’s call these Stunning Successes. 35% of the tasks are on time; we’ll called them Regular Tasks.

15% of the time, you encounter some bad luck.


  • Eight tasks, instead of taking an hour, take two hours. Let’s call those Little Slips.

  • Four tasks (one in 25) end up taking four hours, instead of the hour you thought they’d take. There’s a bug in some library that you’re calling; you need access to a particular server and the IT guys are overextended so they don’t call back until after lunch. We’ll call them Wasted Mornings.

  • Two tasks (one in fifty) take a whole day, instead of an hour. Someone has to stay home to mind a sick kid. Those we’ll call Lost Days.

  • One task in a hundred—just one—takes two days instead of just an hour. A library developed by another team is a couple of days late; a hard drive crash takes down a system and it turns out there’s a Post-It note jammed in the backup tape drive; one of the programmers has her wisdom teeth removed (all these things have happened on projects that I’ve worked on). These don’t have the devastating impact of a Black Swan; they’re like baby Black Swans, so let’s call them Black Cygnets.

Number of tasks Type of task Duration Total (hours)
50 Stunning Success 0.50 25
35 On Time 1.00 35
8 Little Slip 2 16
4 Wasted Morning 4 16
2 Lost Day 8 16
1 Black Cygnet 16 16
100 124

That’s right: the average project, based on the assumptions above, would come in 24% late. That is, you estimated it would take two and a half weeks. In fact, it’s going to take more than three weeks. Mind you, that’s the average project, and the notion of the “average” project is strictly based on probability. There’s no such thing as an “average” project in reality and all of its rich detail. Not every project will encounter bad luck—and some projects will run into more bad luck than others.

So there’s a way of modeling projects in a more representative way, and it can be a lot of fun. Take the probabilities above, and subject them to random chance. Do that for every task in the project, then run a lot of projects. This shows you what can happen on projects in a fairly dramatic way. It’s called a Monte Carlo simulation, and it’s an excellent example of exploratory test automation.

I put together a little Ruby program to generate the results of scenarios like the one above. The script runs N projects of M tasks each, allows me to enter as many probabilities and as many durations as I like, puts the results into an Excel spreadsheet, and graphs them. (Naturally I found and fixed a ton of bugs in my code as I prepared this little project. But I also found bugs in Excel, including some race-condition-based crashes, API performance problems, and severely inadequate documentation. Ain’t testing fun?) For the scenario above, I ran 5000 projects of 100 randomized tasks each. Based on the numbers above, I got these results:

Average Project 123.83 hours
Minimum Length 74.5 hours
Maximum Length 217 hours
On time or early projects 460 (9.2%)
Late projects 4540 (90.8%)
Late by 50% or more 469 (9.8%)
Later by 100% or more 2 (0.9%)
Image: Standard Project

Here are some of the interesting things I see here:


  • The average project took 123.83 hours, almost 25% longer than estimated.

  • 460 projects (or fewer than 10%) were on time or early!

  • 4540 projects (or just over 90%) were late!

  • You can get lucky. In the run I did, three projects were accomplished in 80 hours or fewer. No project avoided having any Wasted Mornings, Lost Days, or Black Cygnets. That’s none out of five thousand.

  • You can get unlucky, too. 469 projects took at least 1.5 times their projected time. Two took more than twice their projected time. And one very unlucky project had four Wasted Mornings, one Lost Day, and eight Black Cygnets. That one took 217 hours.

This might seem to some to be a counterintuitive result. Half the tasks took only half of the time alloted to them. 85% of the tasks came in on time or better. Only 15% were late. There’s a one-in-one-hundred chance that you’ll encounter a Black Cygnet. How could it be that so few projects came in on time?

The answer lies in asymmetry, another element of Taleb’s Black Swan model. It’s easy to err in our estimates by, say, a factor of two. Yet dividing the duration of a task by two has a very different impact from multiplying the duration by two. A Minor Victory saves only half a Regular Task, but a Little Slip costs two whole Regular Tasks.

Suppose you’re pretty good at estimation, and that you don’t underestimate so often. 20% of the tasks came in 10% early (let’s call those Minor Victories). 65% of the tasks come right on time (Regular Tasks). That is, 85% of your estimates are either too conservative or spot on. As before, there are eight Little Slips, four Wasted Mornings, two Lost Days, and a Black Cygnet.

With 20% of your tasks coming in early, and 15% coming in late, how long would you expect the average project to take?

Number of tasks Type of task Duration Total (hours)
20 Minor Victory .9 18
65 On Time 1.00 65
8 Little Slip 2 16
4 Wasted Morning 4 16
2 Lost Day 8 16
1 Black Cygnet 16 16
100 147

That’s right: even though your estimation of tasks is more accurate than in the first example above, the average project would come in 47% late. That is, you thought it would take two and a half weeks, and in fact, it’s going to take more than three and a half weeks. Mind you, that’s the average, and again that’s based on probability. Just as above, not every project will encounter bad luck, and some projects will run into more bad luck than others. Again, I ran 5,000 projects of 100 tasks each.

Average Project 147.24 hours
Minimum Length 105.2 hours
Maximum Length 232 hours
On time or early projects 0 (0.0%)
Late projects 5000 (100.0%)
Late by 50% or more 2022 (40.4%)
Late by 100% or more 30 (0.6%)
Image: Typical Project

Over 5000 projects, not a single project came in on time. The very best project came in just over 5% late. It had 18 Minor Victories, 77 on-time tasks, four Little Slips, and a Wasted Morning. It successfully avoided the Lost Day and the Black Cygnet. And in being anywhere near on-time, it was exceedingly rare. In fact, only 16 out of 5000 projects were less than 10% late.

Now, these are purely mathematical models. They ignore just about everything we could imagine about self-aware systems, and the ways the systems and their participants influence each other. The only project management activity that we’re really imagining here is the modelling and estimating of tasks into one-hour chunks. Everything that happens after that is down to random luck. Yet I think the Monte Carlo simulations shows that, unmanaged, what we might think of as a small number of surprises and a small amount of disorder can have a big impact.

Note that, in both of the examples above, at least 85% of the tasks come in on time or early overall. At most, only 15% of the tasks are late. It’s the asymmetry of the impact of late tasks that makes the overwhelming majority of projects late. A task that takes one-sixteenth of the time you estimated saves you less that one Regular Task, but a Black Cygnet costs you an extra fifteen Regular Tasks. The combination of the mathematics and the unexpected is relentlessly against you. In order to get around that, you’re going to have to manage something. What are the possible strategies? Let’s talk about that tomorrow.

Done, The Relative Rule, and The Unsettling Rule

Thursday, September 9th, 2010

The Agile community (to the degree that such a thing exists at all; it’s a little like talking about “the software industry”) appears to me to be really confused about what “done” means.

Whatever “done” means, it’s subject to the Relative Rule. I coined the Relative Rule, inspired by Jerry Weinberg‘s definition of quality (“quality is value to some person(s)”). The Relative Rule goes like this:

For any abstract X, X is X to some person, at some time.

For example, the idea of a “bug” is subject to the Relative Rule. A bug is not a thing that exists in the world; it doesn’t have a tangible form. A bug is a relationship between the product and some person. A bug is a threat to the value of the product to some person. The notion of a bug might be shared among many people, or it might be exclusive to some person.

Similarly: “done” is “done to some person(s), at some time,” and implicitly, “for some purpose“. To me, a tester’s job is to help people who matter—most importantly, the programmers and the product owner—make an informed decision about what constitutes “done” (and as I’ve said before, testers aren’t there to make that determination themselves). So testers, far from worrying about “done”, can begin to relax right away.

Let’s look at this in terms of a story.

A programmer takes on an assignment to code a particular function. She goes into cycles of test-driven development, writing a unit check, writing code to make the check pass, running a suite of prior unit checks and making sure that they all run green, and repeating the loop, adding more and more checks for that function as she goes. Meanwhile, the testers have, in consultation with the product owner, set up a suite of examples that demonstrate basic functionality, and they automate those examples as checks.

The programmer decides that she’s done writing a particular function. She feels confident. She runs them against the examples. Two examples don’t work properly. Ooops, not done. Now she doesn’t feel so confident. She writes fixes. Now the examples all work, so now she’s done. That’s better.

A tester performs some exploratory tests that exercise that function, to see if it fulfills its explicit requirements. It does. Hey, the tester thinks, based on what I’ve seen so far, maybe we’re done programming… but we’re not done testing. Since no one—not testers, not programmers, not even requirements document writers—imagine!—is perfect, the tester performs other tests that explore the space of implicit requirements.

The tester raises questions about the way the function might or might not work. The tester expands the possibilities of conditions and risks that might be relevant. Some of his questions raise new test ideas, and some of those tests raise new questions, and some of those questions reveal that certain implicit requirements haven’t been met. Not done!

The tester is done testing, for now, but no one is now sure that programming is done. The programmer agrees that the tester has raised some significant issues. She’s mildly irritated that she didn’t think of some of these things on her own, and she’s annoyed that others are not explicit in the specs that were given to her. Still, she works on both sets of problems until they’re addressed too. (Done.)

For two of the issues the tester has raised, the programmer disagrees that they’re really necessary (that is, things are done, according to the programmer). The tester tries to make sure that this isn’t personal, but remains concerned about the risks (things are not done, according to the tester). After a conversation, the programmer persuades the tester that these two issues aren’t problems (oh, done after all), and they both feel better.

Just to be sure, though, the tester brings up the issues with the product owner. The product owner has some information about business risk that neither the tester nor the programmer had, and declares emphatically that the problem should be fixed (not done).

The programmer is reasonably exasperated, because this seems like more work. Upon implementing one fix, the programmer has an epiphany; everything can be handled by a refactoring that simultaneously makes the code easier to understand AND addresses both problems AND takes much less time. She feels justifiably proud of herself. She writes a few more unit checks, refactors, and all the unit checks pass. (Done!)

One of the checks of the automated examples doesn’t pass. (Damn; not done.) That’s frustrating. Another fix; the unit checks pass, the examples pass, the tester does more exploration and finds nothing more to be concerned about. Done! Both the programmer and the tester are happy, and the product owner is relieved and impressed.

Upon conversation with other programmers on the the project team, our programmer realizes that there are interactions between her function and other functions that mean she’s not done after all. That’s a little deflating. Back to the drawing board for a new build, followed by more testing. The tester feels a little pressured, because there’s lots of other work to do. Still, after a little investigation, things look good, so, okay, now, done.

It’s getting to the end of the iteration. The programmers all declare themselves done. All of the unit checks are running green, and all of the ATDD checks are running green too. The whole team is ready to declare itself done. Well, done coding the new features, but there’s still a little uncertainty because there’s still a day left in which to test, and the testers are professionally uncertain.

On the morning of the last day of the iteration, the programmers get into scoping out the horizon for the next iteration, while testers explore and perform some new tests. They apply oracles that show the product isn’t consistent with a particular point in a Request-For-Comment that, alas, no one has noticed before. Aaargh! Not done.

Now the team is nervous; people are starting to think that they might not be done what they committed to do. The programmers put in a quick fix and run some more checks (done). The testers raise more questions, perform more investigations, consider more possibilities, and find that more and more stopping heuristics apply (you’ll find a list of those here: https://www.developsense.com/blog/2009/09/when-do-we-stop-test/). It’s 3:00pm. Okay, finally: done. Now everyone feels good. They set up the demo for the iteration.

The Customer (that is, the product owner) says “This is great. You’re done everything that I asked for in this iteration.” (Done! Yay!) “…except, we just heard from The Bank, and they’ve changed their specifications on how they handle this kind of transaction. So we’re done this iteration (that is, done now, for some purpose), but we’ve got a new high-priority backlog item for next Monday, which—and I’m sorry about this—means rolling back a lot of the work we’ve done on this feature (not done for some other purpose). And, programmers, the stuff you were anticipating for next week is going to be back-burnered for now.”

Well, that’s a little deflating. But it’s only really deflating for the people who believe in the illusion that there’s a clear finish line for any kind of development work—a finish line that is algorithmic, instead of heuristic.

After many cycles like the above, eventually the programmers and the testers and the Customer all agree that the product is indeed ready for deployment. That agreement is nice, but in one sense, what the programmers and the testers think doesn’t matter. Shipping is a business decision, and not a technical one; it’s the product owner that makes the final decision. In another sense, though, the programmers and testers absolutely matter, in that a responsible and effective product owner must seriously consider all of the information available to him, weighing the business imperatives against technical concerns. Anyway, in this case, everything is lined up. The team is done! Everyone feels happy and proud.

The product gets deployed onto the bank’s system on a platform that doesn’t quite match the test environment, at volumes that exceed the test volumes. Performance lags, and the bank’s project manager isn’t happy (not done). The testers diligently test and find a way to reproduce the problem (they’re done, for now).

The programmers don’t make any changes to the code, but find a way to change a configuration setting that works around the problem (so now they’re done). The testers show that the fix works in the test environments and at heavier loads (done). Upon evaluation of the original contract, recognition of the workaround, and after its own internal testing, the bank accepts the situation for now (done) but warns that it’s going to contest whether the contract has been fulfilled (not done).

Some people are tense; others realize that business is business, and they don’t take it personally. After much negotiation, the managers from the bank and the development shop agree that the terms of the contract have been fulfilled (done), but that they’d really prefer a more elegant fix for which the bank will agree to pay (not done). And then the whole cycle continues. For years.

So, two things:

1) Definitions and decisions about “done” are always relative to some person, some purpose, and some time. Decisions about “done” are always laden with context. Not only technical considerations matter; business considerations matter too. Moreover, the process of deciding about doneness is not merely logical, but also highly social. Done is based not on What’s Right, but on Who Decides and For What Purpose and For Now. And as Jerry Weinberg points out, decisions about quality are political and emotional, but made by people who would like to appear rational.

However, if you want to be politically, emotionally, and rationally comfortable, you might want to take a deep breath and learn to accept—with all of your intelligence, heart, and good will—not only the first point, but also the second…

2) “Done” is subject to another observation that Jerry often makes, and that I’ve named The Unsettling Rule:

Nothing is ever settled.

Update, 2013-10-16: If you’re still interested, check out the esteemed J.B. Rainsberger on A Better Path To the “Definition of Done”.

Disposable Time

Sunday, January 17th, 2010

In our Rapid Testing class, James Bach and I like to talk about an underappreciated tester resource: disposable time. Disposable time is the time that you can afford to waste without getting into trouble.

Now, we want to be careful about what we mean by “waste”, here. It’s not that you want to waste the time. You probably want to spend it wisely. It’s just that you won’t suffer harm if you do happen to waste it. Disposable time is to your working hours what disposable income is to your total personal income. (In fact, even that’s not quite correct, strictly speaking; we actually mean discretionary income: the money that’s left over after you’ve paid for all of the things that you must pay for—food, shelter, basic clothing, medical, and tax expenses. The money that people call disposable income is more properly called discretionary income; as Wikipedia says, “the amount of ‘play money’ left to spend or save.” Oh well. We’ll go with the incorrect but popular interpretation of “disposable” here.)

You’re never being scrutinized every minute of every day. Practically everyone has a few moments when no one important is watching. In that time, you might

  • try a tiny test that hasn’t been prescribed.
  • try putting in a risky value instead of a safe value.
  • pretend to change your mind, or to make a mistake, and go back a step or two; users make mistakes, and error handling and recovery are often the most vulnerable parts of the program.
  • take a couple of moments to glance at some background information relevant to the work that you’re doing.
  • write in your journal.
  • see if any of your colleagues in technical support have a hot issue that can inform some test ideas.
  • steal a couple of moments to write a tiny, simple program that will save you some time; use the saved time and the learning to extend your programming skills so that you can solve increasingly complex programming problems.
  • spend an extra couple of minutes at the end of a coffee break befriending the network support people.
  • sketch a workflow diagram for your product, and at some point show it to an expert, and ask if you’ve got it right.
  • snoop around in the support logs for the product.
  • add a few more lines to a spreadsheet of data values
  • help someone else solve a problem that they’re having.
  • chat with a programmer about some aspect of the technology.
  • even if you do nothing else, at least pause and look around the screen as you’re testing. Take a moment or two to recognize a new risk and write down a new question or a new test idea. Report on that idea later on; ask your test lead, your manager, or a programmer, or a product owner if it’s a risk worth investigating. Hang on to your notes. When someone asks “Why didn’t you find that bug,” you may have an answer for them.

If it turns out that you’ve made a bad investment, oh well. By definition, however large or small the period, disposable is time that you can afford to blow without suffering consequences.

On the other hand, you may have made a good investment. You may have found a bug, or recognized a new risk, or learned something important, or helped someone out of a jam, or built on a professional relationship, or surprised and impressed your manager. You may have done all of these things at once. Even if you feel like you’ve wasted your time, you’ve probably learned enough to insulate yourself from wasting more time in the same way. When you discover that an alley is blind, you’re unlikely to return there when there are other things to explore.

In The Black Swan, Nassim Nicholas Taleb proposes an investment strategy wherein you put the vast bulk of your money, your nest egg, in very safe securities. You then invest a small amount—an amount that you can afford to lose—in very speculative bets that have a chance of providing a spectacular return. He call that very improbable high-return event a positive Black Swan. Your nest egg is like the part of your job that you must accomplish. Disposable time is like your Black Swan fund; you may lose it all, but you have a shot at a big payoff. But there’s an important difference, too: since learning is an almost inevitable product of using your disposable time, there’s almost always some modest positive outcome.

We encourage test managers to allow disposable time explicitly for their testers. As an example, Google provides its staff with Innovation Time Off. Engineers are encouraged to spend 20% of their time pursuing projects that interest them. That sounds like a waste, until one learns that Google projects like Gmail, Google News, Orkut, and AdSense came of these investments.

What Google may not know is that even within the other 80% of the time that’s ostensibly on mission, people still have, and are still using, non-explicit disposable time. People have that almost everywhere, whether they have explicit disposable time or not.

If you’re working in an environment where you’re being watched so closely that none of this is possible, and where you’re punished for learning or seeking problems, my advice is to make sure that slavery has been abolished in your jurisdiction. Then find a job where your testing skills are valued and your managers aren’t wasting their time by watching your work instead of doing theirs. But when you’ve got a few moments to fill, fill them and learn something!

Defect Detection Efficiency: An Evaluation of a Research Study

Friday, January 8th, 2010

Over the last several months, B.J. Rollison has been delivering presentations and writing articles and blog posts in which he cites a paper Defect Detection Efficiency: Test Case Based vs. Exploratory Testing [DDE2007], by Juha Itkonen, Mika V. Mäntylä and Casper Lassenius (First International Symposium on Empirical Software Engineering and Measurement, pp. 61-70; the paper can be found here).

I appreciate the authors’ intentions in examining the efficiency of exploratory testing.  That said, the study and the paper that describes it have some pretty serious problems.

Some Background on Exploratory Testing

It is common for people writing about exploratory testing to consider it a technique, rather than an approach. “Exploratory” and “scripted” are opposite poles on a continuum. At one pole, exploratory testing integrates test design, test execution, result interpretation, and learning into a single person at the same time.  At the other, scripted testing separates test design and test execution by time, and typically (although not always) by tester, and mediates information about the designer’s intentions by way of a document or a program. As James Bach has recently pointed out, the exploratory and scripted poles are like “hot” and “cold”.  Just as there can be warmer or cooler water, there are intermediate gradations to testing approaches. The extent to which an approach is exploratory is the extent to which the tester, rather than the script, is in immediate control of the activity.  A strongly scripted approach is one in which ideas from someone else, or ideas from some point in the past, govern the tester’s actions. Test execution can be very scripted, as when the tester is given an explicit set of steps to follow and observations to make; somewhat scripted, as when the tester is given explicit instruction but is welcome or encouraged to deviate from it; or very exploratory, in which the tester is given a mission or charter, and is mandated to use whatever information and ideas are available, even those that have been discovered in the present moment.

Yet the approaches can be blended.  James points out that the distinguishing attribute in exploratory and scripted approaches is the presence or absence of loops.  The most extreme scripted testing would follow a strictly linear approach; design would be done at the beginning of the project; design would be followed by execution; tests would be performed in a prescribed order; later cycles of testing would use exactly the same tests for regression

Let’s get more realistic, though.  Consider a tester with a list of tests to perform, each using a data-focused automated script to address a particular test idea.  A tester using a highly scripted approach would run that script, observe and record the result, and move on to the next test.  A tester using a more exploratory approach would use the list as a point of departure, but upon observing an interesting result might choose to perform a different test from the next one on the list; to alter the data and re-run the test; to modify the automated script; or to abandon that list of tests in favour of another one.  That is, the tester’s actions in the moment would not be directed by earlier ideas, but would be informed by them. Scripted approaches set out the ideas in advance, and when new information arrives, there’s a longer loop between discovery and the incorporation of that new information into the testing cycle.  The more exploratory the approach, the shorter the loop.  Exploratory approaches do not preclude the use of prepared test ideas, although both James and I would argue that our craft, in general, places excessive emphasis on test cases and focusing techniques at the expense of more general heuristics and defocusing techniques.

The point of all this is that neither exploratory testing nor scripted approaches are testing techniques, nor bodies of testing techniques.  They’re approaches that can be applied to any testing technique.

To be fair to the authors of [DDE2007], since publication of their paper there has been ongoing progress in the way that many people—in particular Cem Kaner, James Bach, and I—articulate these ideas, but the fundamental notions haven’t changed significantly.

Literature Review

While the authors do cite several papers on testing and test design techniques, they do not cite some of the more important and relevant publications on the exploratory side.  Examples of such literature include “Measuring the Effectiveness of Software Testers” (Kaner, 2003; slightly updated in 2006); and “Software engineering metrics: What do they measure and how do we know?” (Kaner & Bond, 2004); and “Inefficiency and Ineffectiveness of Software Testing: A Key Problem in Software Engineering” (Kaner 2006; to be fair to the authors, this paper may have been published too late to inform [DDE2007]),  General Functionality and Stability Test Procedure (for Microsoft Windows 2000 Application Certification) (Bach, 2000); Satisfice Heuristic Test Strategy Model (Bach, 2000); How To Break Software (Whittaker, 2002).

The authors of [DDE2007] appear also to have omitted literature on the subject of exploration and its role in learning. Yet there is significant material on the subject, in both popular and more academic literature.  Examples here include Collaborative Discovery in a Scientific Domain (Okada and Simon; note that the subjects are testing software); Exploring Science: The Cognition and Development of Discovery Processes (David Klahr and Herbert Simon); Plans and Situated Actions (Lucy Suchman); Play as Exploratory Learning (Mary Reilly); How to Solve It (George Polya); Simple Heuristics That Make Us Smart (Gerg Gigerenzer); Sensemaking in Organizations (Karl Weick); Cognition in the Wild (Edward Hutchins); The Social Life of Information (Paul Duguid and John Seely Brown); Sciences of the Artificial (Herbert Simon); all the way back to A System of Logic, Ratiocinative and Inductive (John Stuart Mill, 1843).

These omissions are reflected in the study and the analysis of the experiment, and that leads to a common problem in such studies: heuristics and other important cognitive structures in exploration are treated as mysterious and unknowable.  For example, the authors say, “For the exploratory testing sessions we cannot determine if the subjects used the same testing principles that they used for designing the documented test cases or if they explored the functionality in pure ad-hoc manner. For this reason it is safer to assume the ad-hoc manner to hold true.”  [DDE2007, p. 69]  Why assume?  At the very least, one could at least observe the subjects and debrief them, asking about their approaches.  In fact, this is exactly the role that the test lead fulfills in the practice of skilled exploratory testing.  And why describe the principles only as “ad-hoc”?  It’s not like the principles can’t be articulated. I talk about oracle heuristics in this article, and talk about stopping heuristics here; Kaner’s Black Box Software Testing course talks about test design heuristics; James Bach‘s work talks about test strategy heuristics (especially here); James Whittaker’s books talk about heuristics for finding vulnerabilities…

Tester Experience

The study was performed using testers who were, in the main, novices.  “27 subjects had no previous experience in software engineering and 63 had no previous experience in testing. 8 subjects had one year and 4 subjects had two years testing experience. Only four subjects reported having some sort of training in software testing prior to taking the course.”  ([DDE2007], p. 65 my emphasis)  Testing—especially testing using an exploratory approach—is a complex cognitive activity.  If one were to perform a study on novice jugglers, one would likely find that they drop an approximately equal number of objects, whether they were juggling balls or knives.

Tester Training

The paper notes that “subjects were trained to use the test case design techniques before the experiment.” However, the paper does not make note of any specific training in heuristics or exploratory approaches.  That might not be surprising in light of the weaknesses on the exploratory side of the literature review.  My experience, that of James Bach, and anecdotal reports from our clients suggests that even a brief training session can greatly increase the effectiveness of an exploratory approach.

Cycles of Testing

Testing happens in cycles.  In a strongly scripted testing, the process tends to the linear.  All tests are designed up front; then those tests are executed; then testing for that area is deemed to be done.  In subsequent cycles, the intention is to repeat the original tests to make sure that bugs are fixed to check for regression.  By contrast, exploratory testing is an organic and iterative process.  In an exploratory approach, the same area might be visited several times, such that learning from early “reconnaissance” sessions informs further exploration in subsequent “deep coverage” sessions.  The learning from those (and from ideas about bugs that have been found and fixed) informs “wrap-up sessions”, in which tests may be repeated, varied, or cut from new cloth.  No allowance is made for information and learning obtained during one round of testing to inform later rounds.  Yet such information and learning is typically of great value.

Quantitative vs. Qualitative Analysis

In the study, there is a great deal of emphasis placed on quantifying results, on experimental and on mathematical rigour.  However, such rigour may be misplaced when the products of testing are qualitative, rather than quantitative.

Finding bugs is important, finding many bugs is important, and finding important bugs is especially important. Yet bugs and bug reports are by no means the only products of testing.  The study largely ignores the other forms of information that testing may provide.

  • The tester might learn something about test design, and feed that learning into her approach toward test execution, or vice versa. The value of that learning might be realized immediately (as in an exploratory approach) or over time (as in a scripted approach).
  • The tester, upon executing a test, might recognize a new risk or missing coverage. That recognition might inform ideas about the design and choices of subsequent tests.  In a scripted approach, that’s a relatively long loop.  In an exploratory approach, upon noticing a new risk, the tester might choose to note findings for later on.  On the other hand, the discovery could be cashed immediately:  she  might choose to repeat the test, she might perform a variation on the same test, or might alter her strategy to follow a different line of investigation.  Compared to a scripted approach, the feedback loop between discovery and subsequent action is far shorter.  The study ignores the length of the feedback loops.
  • In addition to discovering bugs that threaten the value of the product, the tester might discover issues—problems that threaten the value of the testing effort or the development project overall.
  • The tester who takes an exploratory approach may choose to investigate a bug or an issue that she has found.  This may reduce the total bug count, but in some contexts may be very important to the tester’s client.  In such cases, the quality of the investigation, rather than the number of bugs found, would be important.

More work products from testing can be found here.

“Efficiency” vs. “Effectiveness”

The study takes a very parsimonious view of “efficiency”, and further confuses “efficiency” with “effectiveness”.  Two tests are equally effective if they produce the same effects. The discovery of a bug is certainly an important effect of a test.  Yet there are other important effects too, as noted above, but they’re not considered in the study.

However, even if we decide that bug-finding is the only worthwhile effect of a test, two equally effective tests might not be equally efficient.  I would argue that efficiency is a relationship between effectiveness and cost.  An activity is more efficient if it has the same effectiveness at lower cost in terms of time, money, or resources.  This leads to what is by far the most serious problem in the paper…

Script Preparation Time Is Ignored

The authors’ evaluation of “efficiency” leaves out the preparation time for the scripted tests! The paper says that the exploratory testing sessions took 90 minutes for design, preparation, and execution. The preparation for the scripted tests took seven hours, where the scripted test execution sessions took 90 minutes, for a total of 8.5 hours.  This fact is not highlighted; indeed, it is not mentioned until the eighth of ten pages. (page 68).  In journalism, that would be called burying the lead.  In terms of bug-finding alone, the authors suggest that the results were of equivalent effectiveness, yet the scripted approach took, in total, 5.6 times longer than the exploratory approach. What other problems could the exploratory testing approaches find given seven additional hours?

Conclusions

The authors offer these four conclusions at the end of the paper:

“First, we identify a lack of research on manual test execution from other than the test case design point of view. It is obvious that focusing only on test case design techniques does not cover many important aspects that affect manual testing. Second, our data showed no benefit in terms of defect detection efficiency of using predesigned test cases in comparison to an exploratory testing approach. Third, there appears to be no big differences in the detected defect types, severities, and in detection difficulty. Fourth, our data indicates that test case based testing produces more false defect reports.”

I would offer to add a few other conclusions.  The first is from the authors themselves, but is buried on page 68:  “Based on the results of this study, we can conclude that an exploratory approach could be efficient, especially considering the average 7 hours of effort the subjects used for test case design activities.”  Or, put another way,

  • During test execution
  • unskilled testers found the same number of problems, irrespective of the approach that they took, but
  • preparation of scripted tests increased testing time approximately by a factor of five
  • and appeared to add no significant value.

Now:  as much as I would like to cite this study as a significant win for exploratory testing, I can’t.  There are too many problems with it.  There’s not much value in comparing two approaches when those approaches are taken by unskilled and untrained people.  The study is heavy on data but light on information. There are no details about the bugs that were found and missed using each approach.  There’s no description of the testers’ activities or thought processes; just the output numbers.  There is the potential for interesting, rich stories on which bugs were found and which bugs were missed by which approaches, but such stories are absent from the paper.  Testing is a qualitative evaluation of a product; this study is a quantitative evaluation of testing.  Valuable information is lost thereby.

The authors say, “We could not analyze how good test case designers our subjects were and how much the quality of the test cases affected the results and how much the actual test execution aproach.”  Actually, they could have analyzed that.  It’s just that they didn’t.  Pity.

Why Is Testing Taking So Long? (Part 2)

Wednesday, November 25th, 2009

Yesterday I set up a thought experiment in which we divided our day of testing into three 90-minute sessions. I also made a simplifying assumption that bursts of testing activity representing some equivalent amount of test coverage (I called it a micro-session, or just a “test”) take two minutes. Investigating and reporting a bug that we find costs an additional eight minutes, so a test on its own would take two minutes, and a test that found a problem would take ten.

Yesterday we tested three modules. We found some problems. Today the fixes showed up, so we’ll have to verify them.

Let’s assume that a fix verification takes six minutes. (That’s yet another gross oversimplification, but it sets things up for our little thought experiment.) We don’t just perform the original microsession again; we have to do more than that. We want to make sure that the problem is fixed, but we also want to do a little exploration around the specific case and make sure that the general case is fixed too.

Well, at least we’ll have to do that for Modules B and C. Module A didn’t have any fixes, since nothing was broken. And Team A is up to its usual stellar work, so today we can keep testing Team A’s module, uninterrupted by either fix verifications or by bugs. We get 45 more micro-sessions in today, for a two-day total of 90.

(As in the previous post, if you’re viewing this under at least some versions of IE 7, you’ll see a cool bug in its handling of the text flow around the table.  You’ve been warned!)

Module Fix Verifications Bug Investigation and Reporting
(time spent on tests that find bugs)
Test Design and Execution
(time spent on tests that don’t find bugs)
New Tests Today Two-Day Total
A 0 minutes (no bugs yesterday) 0 minutes (no bugs found) 90 minutes (45 tests) 45 90

Team B stayed an hour or so after work yesterday. They fixed the bug that we found, tested the fix, and checked it in. They asked us to verify the fix this afternoon. That costs us six minutes off the top of the session, leaving us 84 more minutes. Yesterday’s trends continue; although Team B is very good, they’re human, and we find another bug today. The test costs two minutes, and bug investigation and reporting costs eight more, for a total of ten. In the remaining 74 minutes, we have time for 37 micro-sessions. That means a total of 38 new tests today—one that found a problem, and 37 that didn’t. Our two-day today for Module B is 79 micro-sessions.

Module Fix Verifications Bug Investigation and Reporting
(time spent on tests that find bugs)
Test Design and Execution
(time spent on tests that don’t find bugs)
New Tests Today Two-Day Total
A 0 minutes (no bugs yesterday) 0 minutes (no bugs found) 90 minutes (45 tests) 45 90
B 6 minutes (1 bug yesterday) 10 minutes (1 test, 1 bug) 74 minutes (37 tests) 38 79

Team C stayed late last night. Very late. They felt they had to. Yesterday we found eight bugs, and they decided to stay at work and fix them. (Perhaps this is why their code has so many problems; they don’t get enough sleep, and produce more bugs, which means they have to stay late again, which means even less sleep…) In any case, they’ve delivered us all eight fixes, and we start our session this afternoon by verifying them. Eight fix verifications at six minutes each amounts to 48 minutes. So far as obtaining new coverage goes, today’s 90-minute session with Module C is pretty much hosed before it even starts; 48 minutes—more than half of the session—is taken up by fix verifications, right from the get-go. We have 42 minutes left in which to run new micro-sessions, those little two-minute slabs of test time that give us some equivalent measure of coverage. Yesterday’s trends continue for Team C too, and we discover four problems that require investigation and reporting. That takes 40 of the remaining 42 minutes. Somewhere in there, we spend two minutes of testing that doesn’t find a bug. So today’s results look like this:

Module Fix Verifications Bug Investigation and Reporting
(time spent on tests that find bugs)
Test Design and Execution
(time spent on tests that don’t find bugs)
New Tests Today Two-Day Total
A 0 minutes (no bugs yesterday) 0 minutes (no bugs found) 90 minutes (45 tests) 45 90
B 6 minutes (1 bug yesterday) 10 minutes (1 test, 1 bug) 74 minutes (37 tests) 38 79
C 48 minutes (8 bugs yesterday) 40 minutes (4 tests, 4 bugs) 2 minutes (1 test) 5 18

Over two days, we’ve been able to obtain only 20% of the test coverage for Module C that we’ve been able to obtain for Module A. We’re still at less than 1/4 of the coverage that we’ve been able to obtain for Module B.

Yesterday, we learned one lesson:

Lots of bugs means reduced coverage, or slower testing, or both.

From today’s results, here’s a second:

Finding bugs today means verifying fixes later, which means even less coverage or even slower testing, or both.

So why is testing taking so long? One of the biggest reasons might be this:

Testing is taking longer than we might have expected or hoped because, although we’ve budgeted time for testing, we lumped into it the time for investigating and reporting problems that we didn’t expect to find.

Or, more generally,

Testing is taking longer than we might have expected or hoped because we have a faulty model of what testing is and how it proceeds.

For managers who ask “Why is testing taking so long?”, it’s often the case that their model of testing doesn’t incorporate the influence of things outside the testers’ control. Over two days of testing, the difference between the quality of Team A’s code and Team C’s code has a profound impact on the amount of uninterrupted test design and execution work we’re able to do. The bugs in Module C present interruptions to coverage, such that (in this very simplified model) we’re able to spend only one-fifth of our test time designing and executing tests. After the first day, we were already way behind; after two days, we’re even further behind. And even here, we’re being optimistic. With a team like Team C, how many of those fixes will be perfect, revealing no further problems and taking no further investigation and reporting time?

And again, those faulty management models will lead to distortion or dysfunction. If the quality of testing is measured by bugs found, then anyone testing Module C will look great, and people testing Module A will look terrible. But if the quality of testing is evaluated by coverage, then the Module A people will look sensational and the Module C people will be on the firing line. But remember, the differences in results here have nothing to do with the quality of the testing, and everything to do with the quality of what is being tested.

There’s a psychological factor at work, too. If our approach to testing is confirmatory, with steps to follow and expected, predicted results, we’ll design our testing around the idea that the product should do this, and that it should behave thus and so, and that testing will proceed in a predictable fashion. If that’s the case, it’s possible—probable, in my view—that we will bias ourselves towards the expected and away from the unexpected. If our approach to testing is exploratory, perhaps we’ll start from the presumption that, to a great degree, we don’t know what we’re going to find. As much as managers, hack statisticians, and process enthusiasts would like to make testing and bug-finding predictable, people don’t know how to do that such that the predictions stand up to human variability and the complexity of the world we live in. Plus, if you can predict a problem, why wait for testing to find it? If you can really predict it, do something about it now. If you don’t have the ability to do that, you’re just playing with numbers.

Now: note again that this has been a thought experiment. For simplicity’s sake, I’ve made some significant distortions and left out an enormous amount of what testing is really like in practice.

  • I’ve treated testing activities as compartmentalized chunks of two minutes apiece, treading dangerously close to the unhelpful and misleading model of testing as development and execution of test cases.
  • I haven’t looked at the role of setup time and its impact on test design and execution.
  • I haven’t looked at the messy reality of having to wait for a product that isn’t building properly.
  • I haven’t included the time that testers spend waiting for fixes.
  • I haven’t included the delays associated with bugs that block our ability to test and obtain coverage of the code behind them.
  • I’ve deliberately ignored the complexity of the code.
  • I’ve left out difficulties in learning about the business domain.
  • I’ve made a highly simplistic assumptions about the quality and relevance of the testing and the quality and relevance of the bug reports, the skill of the testers in finding and reporting bugs, and so forth.
  • And I’ve left out the fact that, as important as skill is, luck always plays a role in finding problems.

My goal was simply to show this:

Problems in a product have a huge impact on our ability to obtain test coverage of that product.

The trouble is that even this fairly simple observation is below the level of visibilty of many managers. Why is it that so many managers fail to notice it?

One reason, I think, is that they’re used to seeing linear processes instead of organic ones, a problem that Jerry Weinberg describes in Becoming a Technical Leader. Linear models “assume that observers have a perfect understanding of the task,” as Jerry says. But software development isn’t like that at all, and it can’t be. By its nature, software development is about dealing with things that we haven’t dealt with before (otherwise there would be no need to develop a new product; we’d just reuse the one we had). We’re always dealing with the novel, the uncertain, the untried, and the untested, so our observation is bound to be imperfect. If we fail to recognize that, we won’t be able to improve the quality and value of our work.

What’s worse about managers with a linear model of development and testing is that “they filter our innovations that the observer hasn’t seen before or doesn’t understand” (again, from Becoming a Technical Leader.) As an antidote for such managers, I’d recommend Perfect Software, and Other Illusions About Testing and Lessons Learned in Software Testing as primers. But mostly I’d suggest that they observe the work of testing. In order to do that well, they may need some help from us, and that means that we need to observe the work of testing too. So over the next little while, I’ll be talking more than usual about Session-Based Test Management, developed initially by James and Jon Bach, which is a powerful set of ideas, tools and processes that aid in observing and managing testing.

Why Is Testing Taking So Long? (Part 1)

Tuesday, November 24th, 2009

If you’re a tester, you’ve probably been asked, “Why is testing taking so long?” Maybe you’ve had a ready answer; maybe you haven’t. Here’s a model that might help you deal with the kind of manager who asks such questions.

Let’s suppose that we divide our day of testing into three sessions, each session being, on average, 90 minutes of chartered, uninterrupted testing time. That’s four and a half hours of testing, which seems reasonable in an eight-hour day interrupted by meetings, planning sessions, working with programmers, debriefings, training, email, conversations, administrivia of various kinds, lunch time, and breaks.

The reason that we’re testing is that we want to obtain coverage; that is, we want to ask and answer questions about the product and its elements to the greatest extent that we can. Asking and answering questions is the process of test design and execution. So let’s further assume that we break each session into average two-minute micro-sessions, in which we perform some test activity that’s focused on a particular testing question, or on evaluating a particular feature. That means in a 90-minute session, we can theoretically perform 45 of these little micro-sessions, which for the sake of brevity we’ll informally call “tests”. Of course life doesn’t really work this way; a test idea might a couple of seconds to implement, or it might take all day. But I’m modeling here, making this rather gross simplification to clarify a more complex set of dynamics. (Note that if you’d like to take a really impoverished view of what happens in skilled testing, you could say that a “test case” takes two minutes. But I leave it to my colleague James Bach to explain why you should question the concept of test cases.)

Let’s further suppose that we’ll find problems every now and again, which means that we have to do bug investigation and reporting. This is valuable work for the development team, but it takes time that interrupts test design and execution—the stuff that yields test coverage. Let’s say that, for each bug that we find, we must spend an extra eight minutes investigating it and preparing a report. Again, this is a pretty dramatic simplification. Investigating a bug might take all day, and preparing a good report could take time on the order of hours. Some bugs (think typos and spelling errors in the UI) leap out at us and don’t call for much investigation, so they’ll take less than eight minutes. Even though eight minutes is probably a dramatic underestimate for investigation and reporting, let’s go with that. So a test activity that doesn’t find a problem costs us two minutes, and a test activity that does find a problem takes ten minutes.

Now, let’s imagine one more thing: we have perfect testing prowess; that if there’s a problem in an area that we’re testing, we’ll find it, and that we’ll never enter a bogus report, either. Yes, this is a thought experiment.

One day we come into work, and we’re given three modules to test.

The morning session is taken up with Module A, from Development Team A. These people are amazing, hyper-competent. They use test-first programming, and test-driven design. They work closely with us, the testers, to design challenging unit checks, scriptable interfaces, and log files. They use pair programming, and they review and critique each other’s work in an egoless way. They refactor mercilessly, and run suites of automated checks before checking in code. They brush their teeth and floss after every meal; they’re wonderful. We test their work diligently, but it’s really a formality because they’ve been testing and we’ve been helping them test all along. In our 90-minute testing session, we don’t find any problems. That means that we’ve performed 45 micro-sessions, and have therefore obtained 45 units of test coverage.

(And if you’re viewing this under at least some versions of IE 7, you’ll see a cool bug in its handling of the text flow around the table.  You’ve been warned!)

Module Bug Investigation and Reporting
(time spent on tests that find bugs)
Test Design and Execution
(time spent on tests that don’t find bugs)
Total Tests
A 0 minutes (no bugs found) 90 minutes (45 tests) 45
The first thing after lunch, we have a look at Team B’s module. These people are very diligent indeed. Most organizations would be delighted to have them on board. Like Team A, they use test-first programming and TDD, they review carefully, they pair, and they collaborate with testers. But they’re human. When we test their stuff, we find a bug very occasionally; let’s say once per session. The test that finds the bug takes two minutes; investigation and reporting of it takes a further eight minutes. That’s ten minutes altogether. The rest of the time, we don’t find any problems, so that leaves us 80 minutes in which we can run 40 tests. Let’s compare that with this morning’s results.

Module Bug Investigation and Reporting
(time spent on tests that find bugs)
Test Design and Execution
(time spent on tests that don’t find bugs)
Total Tests
A 0 minutes (no bugs found) 90 minutes (45 tests) 45
B 10 minutes (1 test, 1 bug) 80 minutes (40 tests) 41
After the afternoon coffee break, we move on to Team C’s module. Frankly, it’s a mess. Team C is made up of nice people with the best of intentions, but sadly they’re not very capable. They don’t work with us at all, and they don’t test their stuff on their own, either. There’s no pairing, no review, in Team C. To Team C, if it compiles, it’s ready for the testers. The module is a dog’s breakfast, and we find bugs practically everywhere. Let’s say we find eight in our 90-minute session. Each test that finds a problem costs us 10 minutes, so we spent 80 minutes on those eight bugs. Every now and again, we happen to run a test that doesn’t find a problem. (Hey, even dBase IV occasionally did something right.) Our results for the day now look like this:

Module Bug Investigation and Reporting
(time spent on tests that find bugs)
Test Design and Execution
(time spent on tests that don’t find bugs)
Total Tests
A 0 minutes (no bugs found) 90 minutes (45 tests) 45
B 10 minutes (1 test, 1 bug) 80 minutes (40 tests) 41
C 80 minutes (8 tests, 8 bugs) 10 minutes (5 tests) 13
Because of all the bugs, Module C allows us to perform thirteen micro-sessions in 90 minutes. Thirteen, where with the other modules we managed 45 and 41. Because we’ve been investigating and reporting bugs, there are 32 micro-sessions, 32 units of coverage, that we haven’t been able to obtain on this module. If we decide that we need to perform that testing (and the module’s overall badness is consistent throughout), we’re going to need at least three more sessions to cover it. Alternatively, we could stop testing now, but what are the chances of a serious problem lurking in the parts of the module we haven’t covered? So, the first thing to observe here is:
Lots of bugs means reduced coverage, or slower testing, or both.

There’s something else that’s interesting, too. If we are being measured based on the number of bugs we find (exactly the sort of measurement that will be taken by managers who don’t understand testing), Team A makes us look awful—we’re not finding any bugs in their stuff. Meanwhile, Team C makes us look great in the eyes of management. We’re finding lots of bugs! That’s good! How could that be bad?

On the other hand, if we’re being measured based on the test coverage we obtain in a day (which is exactly the sort of measurement that will be taken by managers who count test cases; that is, managers who probably have an even more damaging model of testing than the managers in the last paragraph), Team C makes us look terrible. “You’re not getting enough done! You could have performed 45 test cases today on Module C, and you’ve only done 13!” And yet, remember that in our scenario we started with the assumption that, no matter what the module, we always find a problem if there’s one there. That is, there’s no difference between the testers or the testing for each of the three modules; it’s solely the condition of the product that makes all the difference.

This is the first in a pair of posts. Let’s see what happens tomorrow.

When Do We Stop Testing? One More Sure Thing

Tuesday, October 13th, 2009

Not too long ago, I posted a list of stopping heuristics for testing. As usual, such lists are always subjective, subject to refinement and revision, and under scrutiny from colleagues and other readers. As usual, James Bach is a harsh critic (and that’s a compliment, not a complaint). We’re still transpecting over some of the points; eventually we’ll come out with something on which we agree.

Joe Harter, in his blog, suggests splitting “Pause That Refreshes” into two: “Change in Priorities” and “Lights are Off“. The former kicks in when we know that there’s still testing to be done, but something else is taking precedence. The latter is that we’ve lost our sense of purpose—as I suggested in the original post we might be tired, or bored, or uninspired to test and that a break will allow us to return to the product later with fresh eyes or fresh minds. Maybe they’re different enough that they belong in different categories, and I’m thinking that they are. Joe provides a number of examples of why the lights go out; one feels to me like “customary conclusion”, another looks like “Mission Accomplished”. But his third point is interesting: it’s a form of Parkinson’s Law, “work expands to fill the time available for its completion”. Says Joe, “The test team might be given more time than is actually necessary to test a feature so they fill it up with old test cases that don’t have much meaning.” I’m not sure how often people feel as though they have more time than they need, but I am sure that I’ve seen (been in) situations where people seem to be bereft of new ideas and simply going through the motions. So: if that feeling comes up, one should consider Parkinson’s Law and a Pause That Refreshes. Maybe there’s a new one there. But as Joe himself points out, “In the end it doesn’t matter if you use [Michael’s] list, my list or any list at all. These heuristics are rules of thumb to help thinking testers decide when testing should stop. The most important thing is that you are thinking about it.”

For sure, however, there is a glaring omission in the original list. Cem Kaner pointed it out to me—and that shouldn’t have been necessary, because I’ve used this heuristic myself. It focuses on the individual tester, but it might also apply to a testing or development team.

Mission Rejected. We stop testing when we perceive a problem for some person—in particular, an ethical issue—that prevents us from continuing work on a given test, test cycle, or development project.

Would you continue a test if it involved providing fake test results? Lying? Damaging valuable equipment? Harming a human, as in the Milgram Experiment or the Stanford Prison Experiment? Maybe the victim isn’t the test subject, but the client: Would you continue a test if you believed that some cost of what you were doing—including, perhaps, your own salary—were grossly disproportionate to the value it produced? Maybe the victim is you: Would you stop testing if you believed that the client wasn’t paying you enough?

The consequences of ignoring this heuristic can be dire. Outside the field of software testing, but in testing generally, a friend of mine worked in a science lab that did experiments on bone regeneration. The experimental protocol involved the surgical removal of around one inch of bone from both forelegs of a dog (many dogs, over the course of the research), treating one leg as an experiment and the other as a control. Despite misgivings, my friend was reasonably convinced of the value of the work. Later, when he found out that these experiments had been performed over and over, and that no new science was really being done, he suffered a nervous breakdown and left the field. Sometimes testing doesn’t have a happy ending.

When Do We Stop a Test?

Friday, September 11th, 2009

Several years ago, around the time I started teaching Rapid Software Testing, my co-author James Bach recorded a video to demonstrate rapid stress testing. In this case, the approach involved throwing an overwhelming amount of data at an application’s wizard, essentially getting the application to stress itself out.

The video goes on for almost six minutes. About halfway through, James asks, “You might be asking why I don’t stop now. The reason is that we’re seeing a steadily worsening pattern of failure. We could stop now, but we might see something even worse if we keep going.” And so the test does keep going. A few moments later, James provides the stopping heuristics: we stop when 1) we’ve found a sufficiently dramatic problem; or 2) there’s no apparent variation in the behaviour of the program—the program is essentially flat-lining; or 3) the value of continuing doesn’t justify the cost. Those were the stopping heuristics for that stress test.

About a year after I first saw the video, I wanted to prepare a Better Software column on more general stopping heuristics, so James and I had a transpection session. The column is here. About a year after that, the column turned into a lightning talk that I gave in a few places.

About six months after that, we had both recognized even more common stopping heuristics. We were talking them over at STAR East 2009 when Dale Emery and James Lyndsay walked by, and they also contributed to the discussion. In particular, Dale offered that in combat, the shooting might stop in several ways: a lull, “hold your fire”, “ceasefire”, “at ease”, “stand down”, and “disarm”. I thought that was interesting.

Anyhow, here where we’re at so far. I emphasize that these stopping heuristics are heuristics. Heuristics are quick, inexpensive ways of solving a problem or making a decision. Heuristics are fallible—that is, they might work, and they might not work. Heuristics tend to be leaky abstractions, in that one might have things in common with another. Heuristics are also context-dependent, and it is assumed that they will be used by someone who has the competence and skill to use them wisely. So for each one, I’ve listed the heuristic and included at least one argument for not using the heuristic, or for questioning it.

1. The Time’s Up! Heuristic. This, for many testers, is the most common one: we stop testing when the time allocated for testing has expired.

Have we obtained the information that we need to know about the product? Is the risk of stopping now high enough that we might want to go on testing? Was the deadline artificial or arbitrary? Is there more development work to be done, such that more testing work will be required?

2. The Piñata Heuristic. We stop whacking the program when the candy starts falling out—we stop the test when we see the first sufficiently dramatic problem.

Might there be some more candy stuck in the piñata’s leg? Is the first dramatic problem the most important problem, or the only problem worth caring about? Might we find other interesting problems if we keep going? What if our impression of “dramatic” is misconceived, and this problem isn’t really a big deal?

3. The Dead Horse Heuristic. The program is too buggy to make further testing worthwhile. We know that things are going to be modified so much that any more testing will be invalidated by the changes.

The presumption here is that we’ve already found a bunch of interesting or important stuff. If we stop now, will miss something even more important or more interesting?

4. The Mission Accomplished Heuristic. We stop testing when we have answered all of the questions that we set out to answer.

Our testing might have revealed important new questions to ask. This leads us to the Rumsfeld Heuristic: “There are known unknowns, and there are unknown unknowns.” Has our testing moved known unknowns sufficiently into the known space? Has our testing revealed any important new known unknowns? And a hard-to-parse but important question: Are we satisified that we’ve moved the unknown unknowns sufficiently towards the knowns, or at least towards known unknowns?

5. The Mission Revoked Heuristic. Our client has told us, “Please stop testing now.” That might be because we’ve run out of budget, or because the project has been cancelled, or any number of other things. Whatever the reason is, we’re mandated to stop testing. (In fact, Time’s Up might sometimes be a special case of the more general Mission Revoked, if it’s the client rather than ourselves that have made the decision that time’s up.)

Is our client sufficiently aware of the value of continuing to test, or the risk of not continuing? If we disagree with the client, are we sufficiently aware of the business reasons to suspend testing?

6. The I Feel Stuck! Heuristic. For whatever reason, we stop because we perceive there’s something blocking us. We don’t have the information we need (many people claim that they can’t test without sufficient specifications, for example). There’s a blocking bug, such that we can’t get to the area of the product that we want to test; we don’t have the equipment or tools we need; we don’t have the expertise on the team to perform some kind of specialized test.

There might be any number of ways to get unstuck. Maybe we need help, or maybe we just need a pause (see below). Maybe more testing might allow us to learn what we need to know. Maybe the whole purpose of testing is to explore the product and discover the missing information. Perhaps there’s a workaround for the blocking bug; the tools and equipment might be available, but we don’t know about them, or we haven’t asked the right people in the right way; there might experts available to us, either on the testing team, among the programmers, or on the business side and we don’t realize it. There’s a difference between feeling stuck and being stuck.

7. The Pause That Refreshes Heuristic. Instead of stopping testing, we suspend it for a while. We might stop testing and take a break when we’re tired, or bored, or uninspired to test. We might pause to do some research, to do some planning, to reflect on what we’ve done so far, the better to figure out what to do next. The idea here is that we need a break of some kind, and can return to the product later with fresh eyes or fresh minds.

There’s another kind of pause, too: We might stop testing some feature because another has higher priority for the moment.

Sure, we might be tired or bored, but is it more important for us to hang in there and keep going? Might we learn what we need to learn more efficiently by interacting with the program now, rather than doing work offline? Might a crucial bit of information be revealed by just one more test? Is the other “priority” really a priority? Is it ready for testing? Have we already tested it enough for now?

8. The Flatline Heuristic. No matter what we do, we’re getting the same result. This can happen when the program has crashed or has become unresponsive in some way, but we might get flatline results when the program is especially stable, too—”looks good to me!”

Is the application really crashed, or might it be recovering? Is the lack of response in itself an important test result? Does our idea of “no matter what we do” incorporate sufficient variation or load to address potential risks?

9. The Customary Conclusion Heuristic. We stop testing when we usually stop testing. There’s a protocol in place for a certain number of test ideas, or test cases, or test cycles or variation, such that there’s a certain amount of testing work that we do, and we stop when that’s done. Agile teams (say that they) often implement this approach: “When all the acceptance tests pass, then we know we’re ready to ship.” Ewald Roodenrijs gives an example of this heuristic in his blog post titled When Does Testing Stop? He says he stops “when a certain amount of test cycles has been executed including the regression test”.

This differs from “Time’s Up”, in that the time dimension might be more elastic than some other dimension. Since many projects seem to be dominated by the schedule, it took a while for James and me to realize that this one is in fact very common. We sometimes hear “one test per requirement” or “one positive test and one negative test per requirement” as a convention for establishing good-enough testing. (We don’t agree with it, of course, but we hear about it.)

Have we sufficiently questioned why we always stop here? Should we be doing more testing as a matter of course? Less? Is there information available—say, from the technical support department, from Sales, or from outside reviewers—that would suggest that changing our patterns might be a good idea? Have we considered all the other heuristics?

10. No more interesting questions. At this point, we’ve decided that no questions have answers sufficiently valuable to justify the cost of continuing to test, so we’re done. This heuristic tends to inform the others, in the sense that if a question or a risk is sufficiently compelling, we’ll continue to test rather than stopping.

How do we feel about our risk models? Are we in danger of running into a Black Swan—or a White Swan that we’re ignoring? Have we obtained sufficient coverage? Have we validated our oracles?

11. The Avoidance/Indifference Heuristic. Sometimes people don’t care about more information, or don’t want to know what’s going on the in the program. The application under test might be a first cut that we know will be replaced soon. Some people decide to stop testing because they’re lazy, malicious, or unmotivated. Sometimes the business reasons for releasing are so compelling that no problem that we can imagine would stop shipment, so no new test result would matter.

If we don’t care now, why were we testing in the first place? Have we lost track of our priorities? If someone has checked out, why? Sometimes businesses get less heat for not knowing about a problem than they do for knowing about a problem and not fixing it—might that be in play here?

Update: Cem Kaner has suggested one more:  Mission Rejected, in which the tester himself or herself declines to continue testing.  Have a look here.

Any more ideas? Feel free to comment!