Explainer: the use of artificial intelligence in software testing

Banks, financial services and other firms are rushing to embrace artificial intelligence-powered software testing tools and applications, primarily to reduce costs, increase efficiency and automate many of their testing tasks.

But how should we see this trend, and what exactly is the use and implementation of artificial Intelligence in software testing? What aspects and elements play a role when adopting and integrating this relatively young technology in QA? Let’s dive in.

AI in software testing refers to the application of machine learning, natural language processing, and other AI techniques to improve and automate various aspects of the software testing process.

As mentioned earlier, the use of AI in testing aims to enhance efficiency, accuracy, and reduce human intervention, which traditionally involves repetitive tasks and significant manual effort. Unsurprisingly, banks, financial services firms, insurers and other companies are keen to reduce costs and boost efficiency.

AI application and integration

So how can AI be applied to software testing? Firstly, there is test case generation. AI can analyse the application’s code, requirements, or user behaviour and automatically generate test cases.

By using machine learning models, AI can understand common patterns in the application and produce effective and efficient tests, ensuring comprehensive test coverage with minimal human input.

Secondly, test optimisation. AI can optimize test suites by identifying redundant or obsolete tests. It can prioritize tests based on risk assessment, usage patterns, or potential impact areas in the software. This leads to faster testing cycles and better resource allocation.

Automated execution is another important aspect of AI in QA. AI-powered tools can automate the execution of test cases across different environments, devices, and browsers.

These tools can adapt dynamically to application changes, such as UI modifications or updates in functionality, reducing the need for constant script updates.

Additionally, there is defect prediction and bug detection. AI can analyse historical defect data, log files, and other sources of information to predict where defects are likely to occur in the software.

By identifying potential problem areas early in the development process, AI helps to prevent costly defects from reaching the production stage.

Also worth mentioning is visual testing. AI and computer vision techniques can be used to perform visual testing of user interfaces.

These tools can compare UI screenshots or videos to detect any unintended changes, such as visual bugs, layout issues, or misalignments.

Moreover, self-healing tests are increasingly on the radar of any QA team. AI-driven test automation frameworks can implement ‘self-healing’ capabilities, where the tests automatically adapt to minor changes in the application, such as changes in UI elements. This reduces the need to manually update test scripts when the application undergoes changes.

Then there is, as mentioned earlier, natural language processing for test management. NLP can help in translating human-readable requirements into automated test scripts.

QA teams can write test cases in natural language, and AI tools can convert them into structured test scripts, making the process easier for non-technical stakeholders to contribute to testing efforts.

Finally, last but not least, the root cause analysis. AI is able analyse patterns in bug reports and testing logs to determine the root cause of issues.

By correlating data from different sources, AI can provide insights into why a particular defect occurred, helping developers fix problems more efficiently.

Advantages and challenges

As mentioned earlier, companies are increasingly recognising a host of benefits of AI in software testing, such as faster testing cycles as automation and optimisation improve speed.

Also, increased accuracy because AI models reduce human error and enhance precision, as well as better test coverage: AI identifies gaps in testing that might have been overlooked.

Costs are another important aspect as AI is cost-effective. It reduces the need for manual intervention, lowering costs.

Moreover, continuous improvement: Machine learning algorithms improve over time as they are exposed to more data, becoming smarter in detecting defects and optimising tests.

Having said that, some challenges do remain as AI is not entirely without risks or downsides. Firstly, the immense complexity in integration: integrating AI into existing testing tools and frameworks can be difficult as many QA teams have experienced or are experiencing.

Secondly, training AI models is not too straightforward: AI models require substantial data to train and fine-tune for effectiveness.

Then maintaining AI models can be a challenge as AI-based systems need continuous monitoring and adjustments.

In summary, AI in software testing revolutionises the traditional testing processes by automating repetitive tasks, enhancing test coverage, predicting potential defects, and ultimately improving software quality.

While there are challenges, the growing maturity of AI technologies is making them more accessible and valuable in software testing.


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In September, QA Financial held the London conference of the QA Financial Forum, a global series of conference and networking meetings for software risk managers.

The agenda was designed to meet the needs of software testers working for banks and other financial firms working in regulated, complex markets.

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