How AI can help in Software Testing Lifecycle (STLC)
We all know that any software application before release, needs proper testing. Initially, manual testing was the only way to check whether the application was working as expected or not. Gradually, automation came into the market and eliminated a lot of challenges faced with manual testing. Like many other sectors, Artificial Intelligence (AI) is revolutionizing software testing, making it faster, more accurate and efficient. AI is transforming software testing from a labour-intensive and error-prone process to a quicker and more reliable one. The Global AI-enabled Testing Market size is expected to reach USD 1.4 billion by 2030, rising at a market growth of 17.9% CAGR during the forecast period.
Robotic Process Automation (RPA) also known as software robots uses intelligent automation technologies to perform repetitive tasks of human workers. To get an overview of some of the promising RPA tools in the market read this article. One of the most popular vendors of test automation and RPA has been helping lot of customers globally to improve ROI for their business flows.
Software Testing Life Cycle (STLC) is a systematic procedure to perform testing of a software application to ensure software quality goals are met. Let us see how AI can help in each of the below phases of STLC.
How can AI help in Requirement Analysis?
AI can be used for identifying, prioritizing and validating the requirements of a software system. Natural Language Processing (NLP) is a branch of AI that enables machines to understand and interpret human language, which means NLP can help analyze test requirements. Let us see some of the tools which have been contributing significantly to requirement analysis phase. Worksoft Capture tool allows you to capture actions performed against applications under test. Using this tool the Business Analysts simply perform their routine work by just keeping the Capture ‘ON’. The steps they perform on their system are captured and stored as an XML file which then can be used by the testers as a Requirement file for their automation. In the past, Business Analysts had relied on manual effort to gather and document requirements. Worksoft Capture reduces lot of time and effort to create requirements manually, helping Business Analysts focus on more strategic work.
You must be wondering as to how one avoids duplicate requirements in this case. This is taken care by the tool. After the XML formatted capture file is imported into the Worksoft Certify automation tool, features like “Magic Search” or “AI Search” help you find processes that are similar to a specified process. You can also find out the minute differences between the similar processes using Process Compare option. You may watch Worksoft Certify AI-Driven Magic Search to know more.
In addition, its predictive analysis AI can be help in finding which requirements are most critical, whether there is any conflicting requirements, which features demand the most exhaustive testing and other things like that.
How can AI help in Test Planning?
Currently creating Test plan documents manually is time consuming as well as often leads to human error. AI has the capability to guide the QA lead by learning and understanding various similar other applications and finally providing valuable input. AI has the ability to gather data from defect reports, test execution logs, past project performance, and other test artefacts to establish patterns. AI can marshal this test info to identify high-risk areas and software testing challenges. Some of the areas where AI can help include Scope determination, Risk assessment, Resource allocation, Timeline projections.
AI-driven risk assessments leverage machine learning algorithms to predict future risks based on historical data patterns. By analyzing past security incidents and compliance breaches, AI can identify trends and vulnerabilities, helping organizations pre-emptively mitigate risks before they materialize.
If you are a software professional and have not yet tried OpenAI’s ChatGPT for testing, it is high time you do so. Once you play around, you will understand why it is used by 180.5 million users. Since our focus is testing, below is an experiment to get automatic Test Plan for a business requirement. You will be amazed with the output. It is not perfect but still a great way to get started and speed up your work.
How can AI help in Test Case Design?
Traditional test case creation involves knowing the application functionality in detail and based on that understanding execution steps are written manually. As the test cases are created manually, the number of test cases are usually not enough to cover the entire business process landscape due to the time required to craft the test cases. With the rapidly evolving software market where there are frequent and fast software release cycles, creating test cases manually has increasingly become unviable, both from cost and time perspective. You need solutions which can increase test coverage, reusability and reduce repetitive tasks.
Coming to AI, Generative AI can aid in creating test cases that are both exhaustive and efficient and also can provide a broader coverage across the application landscape. Ideally AI is supposed to learn the application automatically crawling and collecting valuable data like screenshots, page loading time, HTML pages etc. AI algorithms can consume all the collected data, can tease out patterns of the app behaviour and can generate test cases thereby reducing the effort. Read this to know in details.
How can AI help in Test Environment Set-up?
Setting up of environment perfectly for testing is really important for achieving a successful test. If not done so, the test execution will be unsuccessful leading to incomplete testing of the application. Therefore, it is crucial to know what the potential challenges are beforehand and take initiatives accordingly.
AI can help in foreseeing such potential hiccups. It can pre-emptively address issues, ensuring smoother test runs. AI contributes significantly in Test data generation and test environment management.
How can AI help in Test Execution?
The execution phase is where ideas are put into action. AI can assist in real-time to adapt test cases based on interim findings, allocate resources dynamically and predict potential bottlenecks.
Coming to the test maintenance part, if there is any change in the Document Object Model (DOM) or the element property, the automation scripts fail, and testers need to update the automated scripts based on the application changes resulting in significant rework for the testing team. Nowadays some of the AI powered testing tools are trained to identify the DOM changes in the application and intelligently identify the changes to modify the automated scripts accordingly with minimal manual intervention. This approach reduces a lot of the effort of the testers leading to a better ROI. To know more refer this.
How can AI help in Test Closure?
As testing draws to a close, it’s crucial to ensure that every criterion has been met, results are well-documented, and insights are ready for stakeholders. Every project stakeholder is interested to know the overall testing coverage across the landscape. The Worksoft Process Intelligence calculates the value of process automation and optimization with continuous business process insights through intelligent automation. It delivers automation recommendations by highlighting value and targeting areas for increased efficiency, cost reduction, and customer experience.
AI integration with test automation tools
Leaders across the software industry have already started investing in solutions which can help overcome the common challenges faced by many organizations in terms delivery with limited number of resources and where the testers spend a lot of time in manual activity for building and conducting tests. Apart from using test automation and RPA tools you should also be aware of modern AI-powered visual testing tools. One of such visual testing tool is Applitools. Applitools Eyes uses artificial intelligence to help teams quickly ship high-quality applications on any browser or device by replicating the ‘human eye’ and automatically spotting bugs and defects with every release. Check out Visual Artificial Intelligence with Applitools & Worksoft.
Benefits of AI in Software Testing
- Enhance test coverage
- Help in speeding up timelines
- Help in improving automation
- Can provide better accuracy
- Help in customization of regression cycles