Software testing faces severe challenges in today’s fast-paced environment. The complexity of modern applications makes testing hard enough. Of late, many business managers regard testing as an obstacle to fast software release and try to sidestep it. Compromising testing may deliver the short-term objective of timely release. But bug-ridden software can erode customer trust and spell doom for the developers and the business alike.

Technology comes to the rescue to enable faster release without compromising testing. Generative Artificial Intelligence (genAI) automates and speeds up testing, to overcome testing-related challenges.

How Artificial Intelligence (AI) Improves Testing

Today, most software has become very complex, with intricate functionalities and multiple integrations. It becomes impossible to draw up manual test cases for every feature and function. Manual testing is anyway time-consuming and error-prone.

Artificial Intelligence helps testers automate and speed up the testing process.

Traditional AI relies on predefined rules and human-programmed instructions. The testing model does not understand the context of the application. As such, it fails to catch all the bugs or vulnerabilities related to system behaviour or user interactions.

GenAI offers a step-up by applying context to the test models. GenAI makes the testing process more iterative and interactive. It makes the relationship between machines and testers more collaborative.

Improved Test Case Generation

The efficacy of test cases depends on covering all eventualities.

Manual test cases invariably miss some intricate interactions, dependencies, and non-linear behaviours. Testers also overlook irregularities in expected behaviour. Such omissions may lead to unexpected bugs and vulnerabilities.  

GenAI-generated automated test cases cover all possible scenarios and eventualities. It co-opts hidden patterns and anomalies that human testers often miss.

The GenAI testing models employ techniques such as deep learning and neural networks on the code, logs, and specifications. The algorithms analyze data patterns, correlations, dependencies, and logic. It relates the inferences to its training data and generates test cases that cover all possible scenarios.

Consider the complexities brought about by tech fragmentation. Testers struggle to cover all browsers, platforms, and devices when testing apps. GenAI-created test cases cover all possible permutations and combinations.

Tools such as Tricentis Copilot, powered by genAI and LLMs, auto-generate test cases. It also optimises test suites, and auto-detects defects, boosting test efficiency and productivity in a big way.

Creating Test Cases from Visual Prototypes

GenAI also turns visual prototypes into automated test cases. GenAI models leverage computer vision to analyse the layout and relationship between elements. The model uses such insights to identify potential test case scenarios.

Tools such as Tricentis Tosca’s Vision AI scan mockups and simplify the UI for test case creation. It creates test cases during the software design phase, much before code generation. 

Analysing the design early allows for catching potential issues and defects before they become ingrained in the code.

Making Manual Tests More User Friendly

GenAI generates test cases automatically. But automated testing does not suit all eventualities.

Generating manual test cases is still indispensable for:

• Ad-hoc or exploratory testing. 
• Usability testing. Human judgement and observation still have an important role in assessing user experience.
• Situations where user interfaces change constantly. Automated tests become inefficient in such cases.
• Most dynamic environments. Frequent changes make test cases unstable. Constant test case maintenance becomes costly, time-consuming, and error-prone. Also, developing scripts for changing content is difficult to implement.  

GenAI helps users create test cases using natural language descriptions. It reduces the time and effort required for manual test creation.

Platforms such as Tricentis deploy automated bots powered by deep learning and custom-trained LLM. These bots convert high-level instructions into automated test cases.

Prioritising Test Cases

Prioritising test cases has become inevitable in today’s complex software development environment.

The rise of Agile and DevOps methodologies in software development shortens development cycles. The associated CI/CD pipelines depend on frequent and automated testing to ensure software quality. But such an approach makes comprehensive testing a casualty. Testers cannot keep pace with the rapid development cycles if they opt for extensive testing.

Prioritising test cases means testing the most critical functionalities first. The testing team can optimise their resources, and make a trade-off between faster releases and sound testing.

AI-powered models identify the risks associated with each test case. The algorithms analyse historical data, failure rates, usage frequency, and impact of failures. 

GenAI models understand the context of the requirements, enabling more accurate prioritisation. Testers can offer natural language descriptions of requirements and user stories. GenAI models decipher potential risks from such descriptions. Analysing the technical specifications alone may not give a complete picture of the risks.

Improved Resilience

Today’s environment is in a constant state of flux. Applications likewise keep changing, with frequent iterations. Even small changes can have a big impact on functionality.

Typically, when the software code changes, the testing team adopts a blanket approach of running the entire test suite. Such an approach, however, leads to lots of unnecessary tests, leading to higher costs and delays.

AI-powered change intelligence tools offer insights into the changes. It makes explicit how such changes could affect downstream systems or processes. These insights allow testers to focus testing on such areas.

Testers can use genAI to update and sync test scripts with code changes. The training model tracks application changes. It identifies the underlying meaning and context of code changes and updates test cases. Such self-healing capabilities future-proof tests and make the process resilient.

Top testing tools such as Tricentis Tosca and Tricentis Testimcome offer self-healing capabilities.

One big impact area of change intelligence is in SAP. SAP applications link every aspect of the business. As such, testers often undertake comprehensive testing after even small changes. Tools such as Tricentis Live Compare offer AI-powered insights to trace changes. testing becomes fast and effortless.

79% of business executives opine using genAI in app modernisation projects increases agility.

Repurposing Tests

Project teams often repurpose functionality and components. But most enterprises run siloed and opaque systems. One development team may remain unaware of bugs identified and fixed by another team. They often end up spending considerable time and effort reinventing the wheel.

GenAI makes it easy to leverage test cases for multiple projects. GenAI tools organise test cases based on functionality, module, or other relevant criteria. On identifying a defect in one project, the tool identifies similar functionalities in linked projects.

Streamlined Test Execution 

GenAI speeds up and automates test execution as well.

GenAI tools

• Integrate test cases with popular testing frameworks such as Appium and Selenium. GenAI generates code using libraries and APIs of these frameworks, enabling direct integration.
• Generate automated test scripts in Python, Java, JavaScript or other suitable languages.
• Generate scripts to configure browsers, devices, and data, and set up the test environment.
• Creates synthetic test data such as user profiles. For instance, the model creates customer profiles and simulates diverse purchasing behaviours.

Customised Reporting

Testers can use genAI to automate customised testing report generation. These reports, targeting specific stakeholders, offer actionable insights and facilitate informed decision-making.  

For instance, testers can create executive reports that summarise key findings. Side-by-side, they can also generate technical reports with detailed performance data.

GenAI is still evolving, and as the technology matures, it will have an even deeper and more positive impact on software testing and testing cloud-based applications.  But enterprises can leverage the possibilities only with robust platforms such as Tricentis that bring the benefits of genAI to testers.