Testing Workflows – Test-After
- by Timothy Klenke
Originally posted on: http://geekswithblogs.net/TimothyK/archive/2014/05/30/testing-workflows-ndash-test-after.aspxIn this post I’m going to outline a few common methods that can be used to increase the coverage of of your test suite. This won’t be yet another post on why you should be doing testing; there are plenty of those types of posts already out there. Assuming you know you should be testing, then comes the problem of how do I actual fit that into my day job. When the opportunity to automate testing comes do you take it, or do you even recognize it? There are a lot of ways (workflows) to go about creating automated tests, just like there are many workflows to writing a program. When writing a program you can do it from a top-down approach where you write the main skeleton of the algorithm and call out to dummy stub functions, or a bottom-up approach where the low level functionality is fully implement before it is quickly wired together at the end. Both approaches are perfectly valid under certain contexts. Each approach you are skilled at applying is another tool in your tool belt. The more vectors of attack you have on a problem – the better. So here is a short, incomplete list of some of the workflows that can be applied to increasing the amount of automation in your testing and level of quality in general. Think of each workflow as an opportunity that is available for you to take. Test workflows basically fall into 2 categories: test first or test after. Test first is the best approach. However, this post isn’t about the one and only best approach. I want to focus more on the lesser known, less ideal approaches that still provide an opportunity for adding tests. In this post I’ll enumerate some test-after workflows. In my next post I’ll cover test-first. Bug Reporting When someone calls you up or forwards you a email with a vague description of a bug its usually standard procedure to create or verify a reproduction plan for the bug via manual testing and log that in a bug tracking system. This can be problematic. Often reproduction plans when written down might skip a step that seemed obvious to the tester at the time or they might be missing some crucial environment setting. Instead of data entry into a bug tracking system, try opening up the test project and adding a failing unit test to prove the bug. The test project guarantees that all aspects of the environment are setup properly and no steps are missing. The language in the test project is much more precise than the English that goes into a bug tracking system. This workflow can easily be extended for Enhancement Requests as well as Bug Reporting. Exploratory Testing Exploratory testing comes in when you aren’t sure how the system will behave in a new scenario. The scenario wasn’t planned for in the initial system requirements and there isn’t an existing test for it. By definition the system behaviour is “undefined”. So write a new unit test to define that behaviour. Add assertions to the tests to confirm your assumptions. The new test becomes part of the living system specification that is kept up to date with the test suite. Examples This workflow is especially good when developing APIs. When you are finally done your production API then comes the job of writing documentation on how to consume the API. Good documentation will also include code examples. Don’t let these code examples merely exist in some accompanying manual; implement them in a test suite. Example tests and documentation do not have to be created after the production API is complete. It is best to write the example code (tests) as you go just before the production code. Smoke Tests Every system has a typical use case. This represents the basic, core functionality of the system. If this fails after an upgrade the end users will be hosed and they will be scratching their heads as to how it could be possible that an update got released with this core functionality broken. The tests for this core functionality are referred to as “smoke tests”. It is a good idea to have them automated and run with each build in order to avoid extreme embarrassment and angry customers. Coverage Analysis Code coverage analysis is a tool that reports how much of the production code base is exercised by the test suite. In Visual Studio this can be found under the Test main menu item. The tool will report a total number for the code coverage, which can be anywhere between 0 and 100%. Coverage Analysis shouldn’t be used strictly for numbers reporting. Companies shouldn’t set minimum coverage targets that mandate that all projects must have at least 80% or 100% test coverage. These arbitrary requirements just invite gaming of the coverage analysis, which makes the numbers useless. The analysis tool will break down the coverage by the various classes and methods in projects. Instead of focusing on the total number, drill down into this view and see which classes have high or low coverage. It you are surprised by a low number on a class this is an opportunity to add tests. When drilling through the classes there will be generally two types of reaction to a surprising low test coverage number. The first reaction type is a recognition that there is low hanging fruit to be picked. There may be some classes or methods that aren’t being tested, which could easy be. The other reaction type is “OMG”. This were you find a critical piece of code that isn’t under test. In both cases, go and add the missing tests. Test Refactoring The general theme of this post up to this point has been how to add more and more tests to a test suite. I’ll step back from that a bit and remind that every line of code is a liability. Each line of code has to be read and maintained, which costs money. This is true regardless whether the code is production code or test code. Remember that the primary goal of the test suite is that it be easy to read so that people can easily determine the specifications of the system. Make sure that adding more and more tests doesn’t interfere with this primary goal. Perform code reviews on the test suite as often as on production code. Hold the test code up to the same high readability standards as the production code. If the tests are hard to read then change them. Look to remove duplication. Duplicate setup code between two or more test methods that can be moved to a shared function. Entire test methods can be removed if it is found that the scenario it tests is covered by other tests. Its OK to delete a test that isn’t pulling its own weight anymore. Remember to only start refactoring when all the test are green. Don’t refactor the tests and the production code at the same time. An automated test suite can be thought of as a double entry book keeping system. The unchanging, passing production code serves as the tests for the test suite while refactoring the tests. As with all refactoring, it is best to fit this into your regular work rather than asking for time later to get it done. Fit this into the standard red-green-refactor cycle. The refactor step no only applies to production code but also the tests, but not at the same time. Perhaps the cycle should be called red-green-refactor production-refactor tests (not quite as catchy). That about covers most of the test-after workflows I can think of. In my next post I’ll get into test-first workflows.
