|By Sellers Smith||
|November 23, 2013 05:00 PM EST||
Tests are an investment in the quality of any given system. There's always a cost to build, run, and maintain each test in terms of time and resources. There's also a great deal of value to be extracted from running the right test at the right time. It's important to remember that for everything you do test, there's something else you're not testing as a result.
Understanding that some tests are more important than others is vital to creating a useful and fluid test plan capable of catering to modern software development techniques. Traditional waterfall development - where everything is delivered for a specific release date in a finished package - has been succeeded by continuous feature roll outs into live systems. This necessitates a different approach from QA departments.
How Do You Build Good Tests?
You can't design or execute the right tests without understanding the intended purpose of the system. Testers need to have an insight into the end user's expectations. Communication between the product people at the business end, the engineers working on the code, and the test department enables you to score tests in terms of their importance and work out where each test cycle should be focusing.
We can break it down into three simple queries: why, what, and how.
"Why" is a higher level overview that really ties into the business side. It's the big-picture thinking that reveals why you're building the software in the first place. What audience need is your product fulfilling? For example, we need to build an e-commerce website to sell our product to the general public.
"What" is really focused on individual features or functions of a system. Using a shopping cart analogy for an e-commerce website, you might say that users must be able to add and remove items from their shopping cart, or that they shouldn't be able to add something that's out of stock.
"How" relates to the practical application of your testing. How exactly is the software going to be tested? How is the success and failure measured?
Good tests are always going to cover our trio, but it can be a useful exercise to break things down.
If you get too caught up in the "what" and the "how," it's possible to miss the "why" completely and it's the most important element because it dictates that some tests are more important than others. The business case for developing your software in the first place has to remain front and center throughout the project. If you begin to lose sight of what the end user needs, then you could be focusing your efforts in the wrong places. Delivering value to your customers is critical. Good testers and good tests always retain and use an awareness of what the intended audience wants and expects.
One technique we can employ is risk-based analysis of tasks. With risk-based analysis, we can arrive at a numerical value for each test, which gives you a sense of its importance. We can assign a score of between 1 and 9 to each test. At the top end, a score of 9 would be a critical test, and at the other end of the spectrum a score of 1 might indicate a test that only needs to be used sparingly. The value is determined by multiplying two factors:
- Impact to the user: What are they trying to accomplish and what would the impact be if they couldn't? How critical is this action?
- Probability of failure: How likely is it that this code will fail? This is heavily influenced by how new it is and how much testing it has already undergone.
If we return to our e-commerce website analogy then we could take the action of being able to buy goods, clearly that's essential, so it would be assigned a 3. However, the functionality for adding goods to the basket and checking out has been there since day one so it has already been tested quite extensively; however, some new features have been added that could impact the code, so that might result in a score of 2. Multiply the two together and you've got a 6. This figure will change over time, because probability of failure will go up if this part of the system is significantly altered, and it will go down over time if it isn't. There's also a discretionary factor that might lead you to bump that 6 up to a 7 if you feel it's merited.
Testers come up with specific scenarios of how an end user might interact with the software and what their expected outcome would be. A typical test might consist of many steps detailed in a script, but this approach can cause problems. What if a new tester comes in to run the test? Is the intent of the test clear to them? What if the implementation of the feature changes? Perhaps the steps no longer result in the expected outcome and the test fails, but that doesn't necessarily mean that the software is not working as intended.
The steps and scripts are delving into the "how," but if the "what" is distinct from the "how" then there's less chance of erroneous failure. Allow the tester some room for an exploratory approach and you're likely to get better results. If something can be tightly scripted and you expect it to be a part of your regression testing, then there's an argument for looking at automating it.
Adopting a technique like Specification by Example or Behavior Driven Design, you're going to lay each test out in this format:
- Given certain preconditions
- When one or more actions happen
- Then you should get this outcome
Regardless of the specifics of the user interface, or the stops along the way between A and Z, the "Given, When, Then" format covers the essential core of the scenario and ensures that the feature does what it's intended to do, without necessarily spelling out exactly how it should do it. It can be used to generate tables of scenarios that describe the actions, variables, and outcomes to test.
Getting down to the nuts and bolts of how testers will create, document, and run tests, we come to the "how." Since projects are more fluid now and requirements or priorities can change on a daily basis, there needs to be some flexibility in the "how" and a willingness to continually reassess the worth of individual tests. Finding the right balance between automated tests, manually scripted tests, and exploratory testing is an ongoing challenge that's driven by the "what" and the "why."
Traditionally, manual tests have been fully documented as a sequence of steps with an expected result for each step, but this is time-consuming and difficult to maintain. It's possible to borrow from automation by plugging in higher-level actions, or keywords, that refer to a detailed script or a common business action that's understood by the tester. There's also been a move toward exploratory testing, where the intent of the test is defined but the steps are dynamic. Finally, there's a place for disposable testing where you might use a recording tool to quickly document each step in a manual test as you work through it. These tests will need to be redone if anything changes, but as it's a relatively quick process and you're actually testing while you create the test, that's not necessarily a problem.
Each individual test should be viewed as an investment. You have to decide whether to run it, whether it needs to be maintained, or if it's time to drop it. You need to continually assess the value and the cost of each test so that you get maximum value from each test cycle. Never lose sight of the business requirements. When a test fails, ask yourself if it's a problem with the system or a problem with the test, and make sure that you always listen to the business people, the software engineers, and most importantly the customer.
Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
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