The automated testing has been one of the cornerstones of modern software development. It enables development teams to significantly speed up certain testing processes and cut out repetitive, laborious manual tasks. However, due to the very nature of most test automation tools, this approach of testing is also riddled with significant issues.
In many cases, automated testing is seen to give rise to more manual activities.
It is somewhat counter-intuitive to think that automated testing might increase the need for manual activities. This happens due to two factors— the limited testing capacity of any team and the inherent deficiencies of legacy automated testing solutions. To understand this aspect of automated testing, it is important to first understand these two aspects in depth.
Understanding the Problem
The main issue with legacy automated testing platforms is the fact that the automation covers only the execution part of the testing activities due to their reliance on hardcoded selectors like xpath or ID. The test cases need to be generated manually and the tests themselves also have to be scripted manually.
Then again, automated test scripts often have a very high rate of failure. Any little change in the UI of the application can break the test script, which would then need to be manually fixed.
This means that, while the execution of the tests can be automated, the rest of the process including test maintenance still have to be carried out manually.
The other aspect of the issue is the limited test capacity of any development team. Since automated tests can break easily, the execution results need to be verified manually. Analyzing the test results is so important since test failures can be attributed to different reasons such as application bugs, broken tests, or environment-related issues. Identifying the failure reason is also time-consuming, especially for automated test scripts that require engineers familiar with code. This means that every team has to strike a balance between creating new tests and analyzing the results of previous tests. With increasing code coverage, it also becomes increasingly difficult to find adequate time for both of these tasks.
The more automated tests that a team creates and runs, the more results they have on their hands that need manual analysis. On the other hand, a lot of time also needs to be devoted to creating and running new automated tests.
This creates a cascading effect which quickly saturates the testing capacity of the team.
Legacy Test Automation Platforms
With changes and updates in the application and the introduction of new features, automated tests become more liable to break and return unreliable results. Legacy test automation tools rely on selectors to recognize UI elements for each test step. However, small changes in the UI can also cause these selectors to change. A simple change in the overall layout of the UI can cause hundreds of automated tests to break. With more and more frequent releases, this problem becomes increasingly more severe.
Another downside of this approach is the fact that it might become more difficult to distinguish between the failure of an automated test script and a genuine bug in the application. With time, as more automated tests are introduced and the development cycle becomes more complex, the entire process of test creation and maintenance remains manual and becomes more intense. This is at the core of the counterproductive nature of automated testing and the reason why more automated tests often give rise to more manual activities.
This situation also constitutes the ideal scenario for test debt — something that can be devastating for any software development project. Developers then need to make the difficult binary choice of devoting more time to test analysis and maintenance or to start creating more automated tests and ignore the results of broken tests. Over time, this can become a major roadblock and derail the expected timeline of the development project.
How Modern AI-Based Tools Can Solve the Problem
This is a scenario where a next generation test automation solution like Functionize can help overcome this issue by employing resources like Big Data, machine learning, and artificial intelligence. While legacy tools suffer from the use of selectors to identify UI elements, Functionize can draw on machine learning and big data to recognize changes in the UI and prevent errors. This enables a lot of the maintenance overhead to be moved on to the test automation platform instead of becoming a time-consuming manual affair.
Instead of hardcoded static selectors, Functionize uses millions of data points to accurately recognize UI elements and track relevant changes. The implication for this when it comes to test automation is extremely beneficial as the tests can self-heal as and when required. Since this manual activity can now be safely transferred to the test automation platform, development teams can focus their efforts solely on creating new tests. Down the line, this helps improve test coverage and enables teams to avoid crushing test debt.
Key Aspects to Consider
While automated testing is by and large considered a positive in the domain of software development, these counterproductive challenges are sure to give pause to development teams. However, with modern test automation platforms like Functionize, this broad issue can be dealt with to a sizable degree. With Functionize, development teams have the power to automate not just their test execution, but also their test maintenance. Coupled with an easy and intuitive test creation process, this paves the way for more efficient and effective automated testing with minimal downside.