Validating Loop Testing in Software Testing

Software testing is an essential part of the software development life cycle to ensure the quality of a software product. Loop testing, or validating the accuracy of loops, is an especially important component of testing, as it helps to gauge performance, identify loop problems, and fix infinite loops.

However, despite the importance of loop tests, many DevOps teams find testing to be the largest bottleneck within their SDLC. Effective loop tests set the foundation for more complex test types. With the purpose of validating a sequence of continually repeated instructions that are performed until a certain condition is reached, loop tests are a type of Control Structure Testing. They are a component of path testing, data validation testing, and condition testing. 

In white box testing, the code is always visible. Loop testing is considered a white box testing technique, as it inspects and verifies an app’s interior components like code, infrastructure, and integrations, to verify input-output flow and improve usage, design, and security. 

Loop testing evaluates performance and utilization. 

There are three key objectives of loop testing. First, loop testing fixes the infinite loop problem. An infinite loop is a piece of code that continually runs, without an end in sight. Think: a computer continually restarting itself. Typically, an infinite loop is caused by a lack of an end condition, an impossible terminating condition, or an accidentally repeated piece of code. With effective loop testing, infinite loops are avoided.

Loop testing also helps to evaluate performance. Loop testing can help to forecast the performance and facility conditions of an app, and identifies different problems inside the loop. Loop testing endures initialization of every variable used inside the loop.

Loop tests are also used to identify loop initialization issues, like an off-by-one error, equality vs. assignment operator, and the && vs. || operator error. Overall, loop testing is very important for assessing the validity of loop constructs.

Loop testing types vary based on complexity.

There are four main types of loop testing, each corresponding to the complexity of the loop.

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Simple loop testing is where a condition is given and then the loop runs and ends, according to both a true and false occurrence of the condition. This type of loop testing evaluates the condition of the loop and determines if the loop will be terminated after some point in time (essentially checking the infinite loop).

In nested loop testing, one inner loop is nested inside another outside loop. This creates a finite number of loops inside the loop, hence the idea of a “nest.” Nested loop testing allows for action on the data in the outer loop to be repeated and is particularly valuable for testing multidimensional data. 

Concatenated loop testing is performed on the loops that follow each other in the code, or the execution of the next loop after the previous loops terminates. Unstructured loop testing tests a combination of nested and concatenated loops, in no particular order. In unstructured loop testing, the loops are restructured to represent the use of programming techniques.

The pros and cons of loop testing.

With loop testing, testers can project the performance, capability, and functionality of the loop. In this vein, loop testing is also critical to assessing the capacity of a loop. 

Loop testing reveals the problems in a loop including uninitialized variables, starting/initiation issues, routine issues, loop recurrence problems, production bottlenecks, and capabilities issues. While some would argue that the issues found during loop testing aren’t terribly important, these small flaws can impact overall operational performance and capabilities, and ultimately cause more problems later down the line if not dealt with right away.

Loop testing is important to releasing a high-quality app, but many dread the thought of loop testing. Critics of loop testing would point out that loop testing only reveals the loop flaws most commonly seen in lower-level applications, these flaws aren’t particularly subtle. 

Loop testing also unveils flaws within the operating system, which can then lead to other, bigger problems like memory boundary violations, detectable pointer errors, and other performance issues.

Regardless of the loop, all loop testing involves testing when the loop is entered, during the loop execution, and when the loop is left, or terminated. As loops become more complex, and projects become larger, loop testing becomes more involved and tedious. Loop testing can be a time-consuming process, eating up developer time and dragging down developer experience and release velocity.

Complex loop testing can be a drain on resources and your cycle times. Increase testing velocity with Predictive Test Selection.

While testing loop scenarios is critical for assuring a program doesn’t go into an infinite loop and assures capacity, loop testing is done at lower-level applications. There are ways to optimize your loop testing, but if your team struggles with overall testing bottlenecks throughout the software testing life cycle, your team likely needs to prioritize a data-driven pipeline.

Launchable is helping organizations apply machine learning to their testing process to help developers move faster.

Run intelligent tests that enable you to ship with more confidence with Launchable’s Predictive Test Selection. Our machine learning model uses your own test suite data to automatically identify and run the tests with the highest probability of failing, based on code changes and test metadata. 

Run fewer tests, while maintaining a high level of confidence that problems will be uncovered. It’s time to stop running massive test suites and running wasteful tests for every single change. Take a data-driven approach to decrease your overall testing life cycle and improve the efficiency of your testing workflows.