As I mentioned in a prior post: Software Testing is a Game, two dominant manual testing approaches to the software testing game are scripted and exploratory testing. In the test automation space, we have other approaches. I look at three main contexts for test automation:
- Code context – eg. unit testing
- System context – eg. protocol or message level testing
- Social context – eg. GUI testing
In each context, the automation approach, tools and styles differ. (Note: I first introduced this idea publicly in my keynote “Test Automation: Why Context Matters” at the Alberta Workshop on Software Testing, May 2005)
In the code context, we are dominated now by automated unit tests written in some sort of xUnit framework. This type of test automation is usually carried out by programmers who write tests to check their code as they develop products, and to provide a safety net to detect changes and failures that might get introduced as the code base changes over the course of a release. We’re concerned that our code works sufficiently well in this context. These kinds of tests are less about being rewarded for finding bugs – “Cool! Discovery!” and more about providing a safety net for coding, which is a different high value activity that can hold our interest.
In the social context, we are concerned about automating the software from a user’s perspective, which means we are usually creating tests using libraries that drive a User Interface, or GUI. This approach of testing is usually dominated by regression testing. People would rather get the tool to repeat the tests than deal with the repetition inherent in regression testing, so they use tools to try to automate that repetition. In other words, regression testing is often outsourced to a tool. In this context, we are concerned that the software works reasonably well for end users in the places that they use it, which are social situations. The software has emergent properties by combining code, system and user expectations and needs at this level. We frequently look to automate away the repetition of manual testing. In video game design terms, we might call repetition that isn’t very engaging as “grinding“. (David McFadzean introduced this idea to me during a design session.)
The system context is a bit more rare, but we test machine to machine interaction, or simulate various messaging or user traffic by sending messages to machines without using the GUI. There are integration paths and emergent properties that we can catch at this level that we will miss with unit testing, but by stripping the UI away, we can create tests that run faster, and track down intermittent or other bugs that might be masked by the GUI. In video or online games, some people use tools to help enhance their game play at this level, sometimes circumventing rules. In the software testing world, we don’t have explicit rules against testing at this level, but we aren’t often rewarded for it either. People often prefer we look at the GUI, or the code level of automation. However, you can get a lot of efficiency for testing at this level by cutting out the slow GUI, and we can explore the emergent properties of a system that we don’t see at the unit level.
We also have other types of automation to consider.
Load and performance testing is a fascinating approach to test automation. As performance thought leaders like Scott Barber will tell you, performance testing is roughly 20% of the automation code development and load generation work, and 80% interpreting results and finding problem areas to address. It’s a fascinating puzzle to solve – we simulate real-world or error conditions, look at the data, find anomalies and investigate the root cause. We combine a quest with discovery and puzzle solving game styles.
If we look at Test-Driven Development with xUnit tools, we even get an explicit game metaphor: The “red bar/green bar game.” TDD practitioners I have worked with have used this to describe the red bar (test failed), green bar (test passed) and refactor (improve the design of existing code, using the automated tests as a safety net.) I was first introduced to the idea of TDD being a game by John Kordyback. Some people argue that TDD is primarily a design activity, but it also has interesting testing implications, which I wrote about here: Test-Driven Development from a Conventional Software Testing Perspective Part 1, here: Test-Driven Development from a Conventional Software Testing Perspective Part 2, and here: Test-Driven Development from a Conventional Software Testing Perspective Part 3.
(As an aside, the Session Tester tool was inspired by the fun that programmers express while coding in this style.)
Cem Kaner often talks about high volume test automation, which is another approach to automation. If you automate a particular set of steps, or a path through a system and run it many times, you will discover information you might otherwise miss. In game design, one way to deal with the boredom of grinding is to add in surprises or rewarding behavior when people repeat things. That keeps the repetitiveness from getting boring. In automation terms, high volume test automation is an incredibly powerful tool to help discover important information. I’ve used this particularly in systems that do a lot of transactions. We may run a manual test several dozen times, and maybe an automated test several hundred or a thousand times in a release. With high volume test automation, I will run a test thousands of times a day or overnight. This greatly increases my chance of finding problems that only appear in very rare events, and forces seemingly intermittent problems to show themselves in a pattern. I’ve enhanced this approach to mutate messages in a system using fuzzing tools, which helps me greatly extend my reach as a tester over both manual testing, and conventional user or GUI-based regression automated testing.
Similarly, creating simulators or emulators to help generate real-world or error conditions that are impossible to create manually are powerful approaches to enhance our testing game play. In fact, I have written about some of these other approaches are about enhancing our manual testing game play. I wrote about “interactive automated testing” in my “Man and Machine” article and in Chapter 19 of the book “Experiences of Test Automation“. This was inspired by looking at alternatives to regression testing that could help testers be more effective in their work.
In many cases, we attempt to automate what the manual testers do, and we fail because the tests are much richer when exercised by humans, because they were written by humans. Instead of getting the computer to do things that we are poor at executing (lots of simple repetition, lots of math, asynchronous actions, etc.) we try to do an approximation of what the humans do. Also, since the humans interact with a constantly changing interface, the dependency of our automation code on a changing product creates a maintenance nightmare. This is all familiar, and I looked at other options. However, another inspiration was code one of my friends wrote to help him play an online game more effectively. He actually created code to help him do better in gaming activities, and then used that algorithm to create an incredibly powerful Business Intelligence engine. Code he wrote to enhance his manual game play in an online game was so powerful at helping him do better work in a gaming context, when he applied it to a business context, it was also powerful.
Software test automation has a couple of gaming aspects:
- To automate parts of the manual software testing game we don’t enjoy
- It’s own software testing game based on our perceived benefits and rewards
In number 1 above, it’s interesting to analyze why we are automating something. Is it to help our team and system quality goals, or are we merely trying to outsource something we don’t like to a tool, rather than look at what alternatives fit our problem best? In number 2, if we look at how we reward people who do automation, and map automation styles and approaches to our quality goals or quality criteria, not to mention helping our teams work with more efficiency, discover more important information, and make the lives of our testers better, there are a lot of fascinating areas to explore.