(Edit: was Four Dimensions of Exploratory Testing. Reader feedback has shown that I missed a dimension.)
I’ve been working on analyzing what I do when Exploratory Testing. I’ve found that describing what I actually do when testing can be difficult, but I’m doing my best to attempt to describe what I do when solving problems. One area that I’ve been particularly interested in lately is intermittent failures. These often get relegated to bug purgatory by being marked as “Non-reproducible” or “unrepeatable bugs”. I’m amazed at how quickly we throw up our hands and “monitor” the bugs, allowing them to languish sometimes for years. In the meantime, a customer somewhere is howling, or quietly moving on to a competitor’s product because they are tired of dealing with it. If only “one or two” customers complain, I know that means there are many others who are not speaking up and possibly quietly moving to a competitor’s product.
So what do I do when Exploratory Testing when I track down an intermittent defect? I started out trying to describe what I do with an article for Better Software called Repeating the Unrepeatable Bug, and I’ve been mulling over concepts and ideas. Fortunately, James Bach has just blogged about How to Investigate Intermittent Problems which is an excellent, thorough post describing ideas on how to make an intermittent bug a bug that can be repeated regularly.
When I do Exploratory Testing, it is frequently to track down problems after the fact. When I test software, I look at risk, I draw from my own experience testing certain kinds of technology solutions, and I use the software. As I use the software, I inevitably discover bugs, or potential problem areas, and often follow hunches. I constantly go through a conjecture and refutation loop where I almost subconciously posit an idea about an aspect of the software, and design a test to decide whether my conjecture is falsifiable or not. (See the work of Karl Popper for more on conjectures and refutations and falsifiability.) It seems that I do this so much, I rarely think about it. Other times, I very consciously follow the scientific method, and design an experiment with controlled variables, a manipulated variable, and observe the responding variables.
When I spot an intermittent bug, I begin to build a theory about it. My initial theory is usually wrong, but I keep gathering data, altering it, and following the conjecture/refutation model. I draw information from others as I gather information and build the theory. I run the theory by experts in particular areas of the software or system to get more insight.
When I do Exploratory Testing to track down intermittent failures, these are five dimensions that I consider:
- Tools & Techniques
This means having the right build, installed and configured properly. This is usually a controlled variable. This must be right, as a failure may occur at a different rate depending on the build. I record what builds I have been using, and the frequency of the failure on a particular build.
Taking the environment into account is a big deal. Installing the same build on slightly different environments can have an impact on how the software responds. This is another controlled variable that can be a challenge to maintain, especially if the test environment is used by a lot of people. Failures can manifest themselves differently depending on the context where they are found. For example, if one test machine has less memory than another, it might exacerbate the underlying problem. Sometimes knowing this information is helpful for tracking it down, so I don’t hesitate to change environments if an intermittent problem occurs more frequently in one than another, using the environment a manipulated variable.
When we start learning to track down bugs when we find them in a product, we learn to repeat exactly what we were doing prior to the bug occurring. We repeat the steps, repeat the failure, and then weed out extraneous information to have a concise bug report. With intermittent bugs, these details may not be important. In many cases I’ve seen defect reports for the same bug logged as several separate bugs in a defect database. Some of them have gone back for two or three years. We seldom look for patterns, instead, we focus on actions. With intermittent bugs, it is important to weed out the details, and apply an underlying pattern to the emerging theory.
For example, if a web app is crashing at a certain point, and we see SQL or database connection information in a failure log, a conjecture might be: “Could it be a database synchronization issue?” Through collaboration with others, and using tools, I could find information on where else in the application the same kind of call to a database is made, and test each scenario that makes the same kind of call to try to refute that conjecture. Note that this conjecture is based on the information we have available at the time, and is drawn from inference. It isn’t blind guesswork. The conjecture can be based on inference to the best explanation of what we are observing, or “abductive inference”.
A pattern will emerge over time as this is repeated, and more information is drawn in from outside sources. That conjecture might be false, so I adjust and retest and record the resulting information. Once a pattern is found, the details can be filled in once the bug is repeatable. This is difficult to do, and requires patience and introspection as well as collaboration with others. This introspection is something I call “after the fact pattern analysis”. How do I figure out what was going on in the application when the bug occured, and how do I find a pattern to explain what happened? This emerges over time, and may change directions as more information is gathered from various sources. In some cases, my original hunch was right, but getting a repeatable case involved investigating the other possibilities and ruling them out. Aspects from each of these experiments shed new light on an emerging pattern. In other cases, a pattern was discovered by a process of elimination where I moved from one wrong theory to the next in a similar fashion.
The different patterns that I apply are the manipulated variables in the experiment, and the resulting behavior is the responding variable. Once I can repeat the responding variable on command, it is time to focus on the details and work with a developer on getting a fix.
Patterns are probably the most important dimension, and reader feedback shows I didn’t go into enough detail in this section. I’ll work on the patterns dimension and explain it more in another post.
When we focus on technical details, we frequently forget about people. I’ve posted before about creating a user profile, and creating a model of the user’s environment. James Bach pointed me to the work of John Musa who has done work in software reliability engineering. The combination of the user’s profile and their environment I was describing is called an “operational profile”.
I also rely heavily on collaboration when working on intermittent bugs. Many of these problems would have been impossible for me to figure out without the help and opinions of other testers, developers, operations people, technical writers, customers, etc. I recently described this process of drawing in information at the right time from different specialists to some executives. They commented that it reminded them of medical work done on a patient. One person doesn’t do it all, and certain health problems can only be diagnosed with the right combination of information from specialists applied at just the right time. I like the analogy.
Tools & Techniques
When Exploratory Testing, I am not only manually testing, but also use whatever tools and techniques help me build a model to describe the problem I’m trying to solve. Information from automated tests, log analyzers, looking at the source code, the system details, anything might be relevant to help me build a model on what might be causing the defect. As James Bach and Cem Kaner say, ET isn’t a technique, it’s a way of thinking about testing. Exploratory Testers use diverse techniques to help gather information and test out theories.
I refer to using many automated or diagnostic testing tools to a term I got from Cem Kaner: “Computer Assisted Testing.” Automated test results might provide me with information, while other automated tests might help me repeat an intermittent defect more frequently than manual testing alone. I sometimes automate certain features in an application that I run while I do manual work as well which I’ve found to be a powerful combination for repeating certain kinds of intermittent problems. I prefer the term Computer Assisted Testing over “automated tests” because it doesn’t imply that the computer takes the place of a human. Automated tests still require a human brain behind them and to analyze their results. They are a tool, not a replacement for human thinking and testing.
Next time you see a bug get assigned to an “unrepeatable” state, review James’ post. Be patient, and don’t be afraid to stand up to adversity to get to the cause. Together we can wipe out the term “unrepeatable bug”.