How we can leverage Artificial Intelligence to Automate Software Application Testing
November 30, 2021 | 7 min read
Today, saying that a lot of hype surrounds Artificial Intelligence would be an understatement, as we see the term associated with a plethora of new products and technologies. Much of it is fluff, as is the case with any new and booming technology, but the rest of it are really specific use cases where AI truly shines. But what exactly is AI? Generally, AI is referred to as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.” or in simple words, it is the ability of a computer program or a machine to think and learn. AI and its peripheral topics are probably the hottest things right now, and we will definitely see it finding its way in seeping into a variety of industries and domains.
One such industry where AI has already begun shining is automation of tests. Testing is a field that requires continuous monitoring and maintenance, and thus any approach that aids in minimizing the manual intervention needed in testing will go a long way in saving large amounts of time and capital.
Shortcomings of conventional test automation:
Software testing is at the heart of IT projects, as the main priority of any delivered software is making it error free. This is ensured by a collaborative effort by testing and quality assurance teams. However, once a codebase becomes large and complex which is generally the case, it becomes difficult to handle. A frequently faced challenge by many teams is maintaining large IT systems, especially ones that are distributed. This makes it resource intensive and time consuming. This makes automating the tests an excellent solution. Automated tests have the potential to reduce a large amount of manual labor. However, a lot of tests are difficult to automate, and hence they end up as manual tests. Whereas tests that are automated are generally easier to implement manually and are not a hassle to the testing and quality assurance teams. Moreover, automating tests with tools like selenium requires a skilled team with experience in the codebase they are currently working in as well as a stronghold over testing methodologies and software engineering practices. The process of hiring and deploying skilled test engineers is very long and costly.
Artificial Intelligence in software testing:
AI-driven testing systems have shown the potential to solve the problems mentioned above. But how is that really possible? Let’s see how AI helps in automating tests — The automation is divided into three phases:
- Training: In this phase, an algorithm is trained on data specific to the organization at hand. This data may or may not include codebase, application interface, logs, test cases, and other specific documents. During the training phase, the algorithm learns the coding practices defined by the organization, identifies how certain elements are supposed to behave, and thus learns what tests should be generated.
- Inference: In this phase, the trained algorithm generates test cases based on respective use cases. It checks the generated tests for various testing parameters like code coverage, accuracy, completeness, and also performs tests in certain conditions. After inference is performed, testers validate the generated output and ensure that it is usable.
- Continual Learning: The goal with continual learning is to continuously retrain the model with new data so as to reduce bias and provide information to the model on cases that were not included in the initial dataset. This helps in improving the accuracy and effectiveness of the existing trained networks.
Applications of AI in test automation:
Let’s take a closer look at some applications where artificial intelligence is helping teams in automating software testing:
- Update and creation of unit tests: Unit tests play an integral role in the continuous testing, integration, and delivery of software. Tests generated from trained models are actual code and not just stubs. This is a significant improvement over conventional template-based automated unit test generation. Unit tests can thus be set up and deployed very quickly as the developers only need to modify it a bit to make it ready for production.
- Automated test maintenance: Test maintenance is updating existing tests based on the changes to the UI so that these changes do not break the test suite. In larger projects, small changes are very frequent and it is difficult to keep track of all of them. Testing tools backed by AI algorithms can automatically fix tests that don’t align with newly made changes.
- Increasing confidence levels: Establishing confidence levels is the process of estimating the performance of ML algorithms on unseen data. Developers use test scripts that capture different attributes and elements of the software. Any relevant information thus collected is fed to the machine learning model. Once this process is completed, the actual testing phase begins. In case of a deviation present in some use cases, the model can still use data to continue with the tests.
- Triage and assignment of bugs: AI systems perform clustering on detected bugs and once they heap together on specific features, and then they are classified based on their severity levels. Bugs with the highest priority or those that require immediate attention are solved first. Every bug is automatically assigned to an assignee based on the past experience of the assignee and the features of the bug itself. This allows for a complete end-to-end automated system, where bugs are detected, classified, prioritized, and finally assigned to the concerned employees.
The benefits of using such systems are:
- Helps shorten software development lifecycles
- Improves code coverage
- Facilitates parallel testing
- Reduces delays in regression testing
- Eliminates the need for a skilled testing team
- Significantly reduces the scope for human error
Challenges we face:
Although AI-backed testing systems are extremely effective, they do have some drawbacks. Let’s look at a few examples of the challenges these systems face:
- AI-backed testing systems mirror the code on which they are trained. It does not understand the intended functionality of a block of code as the term ‘artificial intelligence’ seems to mean.
- Developers need to constantly monitor and validate the generated outputs as even a small error may break the existing functioning unit tests.
- The end-to-end testing pipeline is not completely automated as there needs to be a human in the loop for validation.
- Any AI system is computationally and monetarily expensive in the initial phase.