Programmers develop the software in an incremental and iterative way – the first working version is created and the more version numbers are, the more improvements the software has. Non-software professionals often may not understand how seemingly unrelated parts of the software can be connected by dependencies. Therefore, even minor changes can cause unexpected side effects. To solve this problem, the specialists must regularly test all the parts of the software, even those that have been already tested earlier. It’s repetitive work, so to speed up the testing process automation could be used. Artificial intelligence (AI) use is one of the new ways to automate the testing process and make sure that the software complies with standards.
The article is written by Dmitry Baraishuk, the Chief Innovation Officer (CINO) at a custom software development company Belitsoft (a Noventiq company). The research is based on the Belitsoft test automation portfolio. This agency proved its 20+ years of expertise with a 4,9/5 score from clients of all sizes on the most credible B2B review platforms (Gartner, G2, and Goodfirms). Supporting best practices and delivering expertise from multiple domains, such as Healthcare, E-commerce, FinTech, EdTech, and more, Belitsoft provides its customers with a vast pool of Python software engineers. They are seasoned in architecting, automation testing, implementing QA processes, and more for US-based startups and enterprises.
How Did Programmers Test Software Before AI?
The process was slower due to combining manual and automated approaches. The team used a range of manual methodologies to test the software and then made it again and again to get sustainable results. It was expensive, as the process was scrupulous and labor-intensive.
Then automation tools united with manual methods and open-source frameworks solved the problem, but not perfectly. AI implementation in the software testing processes allowed to replace a partially manual and partially automatic process with a fully automatic one.
What Are the Cons of Traditional Software Testing?
Developers face various common challenges from slow test execution to problems with test cases and script maintenance. The most popular are the following ones:
- Sluggish test execution. The test execution is much slower due to badly developed test scripts, UI automation, insufficient sequencing of test cases, and so on.
- Excessive script maintenance. Even the small change in the application’s UI needs the test script changes. The test maintenance requires more than half of the total testing efforts.
- Problems with test cases. Even renaming a component or other minor changes in the code can cause test cases to break.
- Difficulties in maintaining and managing test data. Programmers who make tests have to maintain test data via building test data generation scripts, utilizing version control, and so forth.
What Are the Benefits of Leveraging AI in Automation Testing?
Programmers use AI for self-healing test automation, dynamic test case generation, cognitive test capabilities, and smart prioritization of test execution. AI testing tools use visual models to develop test scripts. It finds potential defects, automates difficult tasks, and adapts automatically to app changes.
Here are other pros of using AI in software testing:
- Creation and execution of the tests without code. Visual models for test automation allow testers to eliminate learning complex programming languages and various frameworks to execute tests. Thanks to this, even non-tech experts can take part in the testing process.
- Self-healing option. Test scripts can adapt even to minor app changes because of dynamic self-healing and updating. Testers don’t need to test and maintain the software manually again and again, with AI.
- Learning after completing tasks. Testers give AI tasks and after each task, it learns how to better the process. The more testers use AI, the smarter it becomes. Moreover, intelligently understanding the prompts helps AI to generate and maintain test data automatically.
- Advanced analytics. The AI tools can detect the info that is more difficult to identify and transform it into comprehensive analytics. With AI, programmers can find troubled or weak areas that may be missed otherwise.
What Types of Software Testing Does AI Improve?
The software development life cycle (SDLC) includes different tasks and steps that QA engineers have to check. AI can make unit testing, visual testing, and functional and non-functional testing better. Let’s look at each area in detail.
Usually, unit testing checks code units individually but with AI it’s possible to analyze the behavior and structure of the code to create automatic test cases. Also, AI identifies hidden scenarios that programmers could miss even after detailed manual testing. The AI tools pinpoint bug-prone areas via analyzing code patterns.
Examples:
- Generating suits of tests: QA teams analyze the levels of code coverage and use AI to generate a suite of unit tests that will address the uncovered lines of code.
- Automatic adjusting: AI software can be taught to adapt to code alterations. For example, developers have changed the code in the background. The AI-powered testing software will see the change and adjust tests accordingly.
Functional testing can be improved by AI studying the behavior of users – understanding how customers interact with the system, and analyzing critical customer flows to pinpoint the most common users’ actions and prioritize testing those areas. Data-driven tests are a large part of functional testing, so AI testers can automate them to spend more time on strategic tasks. AI tools check how the system will work in real conditions with the help of intelligent test data that are similar to real users’ inputs.
Examples:
- Using AI tools in contextual testing: testers formulate a command in simple English, and the software understands it without predefined scripts. AI tools train on the company’s strategies. Then, they examine uploaded user stories. Finally, the tool is given a website and a type of test that should be performed.
- Analyzing bugs: AI gathers testing data, analyzes it, and makes conclusions about the root cases. It also processes the testing faster, which allows for faster feedback service and an increased number of iterations at a given period.
- Improving manual testing: during exploratory testing, engineers have an opportunity to consult AI about high-priority areas. The algorithm also provides test scenarios and steps in seconds.
In non-functional testing areas, AI distributes resources smartly and takes care of their optimal usage for evaluating performance. Dynamic system changes evolve together with AI-backed tools, and it keeps tests dependable. Also, AI helps to examine performance ahead of the curve, testers sift through historical data and find and eliminate possible bottlenecks.
Examples:
- Prioritizing tests: QA engineers provide AI with contextual datasets, historical data, and testing logs. After that, the AI suggests applying testing efforts to certain areas of the app. The algorithm analyses where the tests are likely to fail and proposes focusing there.
- Security vulnerability detection: AI software assesses the system and identifies weak passwords, gaps in APIs, unauthorized access attempts, and other hazardous activities.
- Real-world testing environment simulation: for example, to conduct load testing QA specialists need numerous user logins and visitors to the website. They provide the AI software with these figures and the system creates appropriate conditions for testing. In the same way, the tool evaluates the performance at peak loads and defines bottlenecks.
Automated visual testing processes benefit from visual regression testing because AI compares screenshots and identifies changes in the user interface that influence user experience.
Examples:
- Pixel-by-pixel testing: AI tools compare UI elements and measure differences in rendering images depending on various screen sizes and resolutions.
What Are AI Software Testing Best Practices?
There are certain pieces of advice that QA experts share with colleagues.
- Test the algorithm. To be on the safe side, always test the algorithm that you are going to integrate into the application. Test it on the data that is generated by the app.
- Complement AI testing with other tools. Although AI-powered testing is fast, it may not be sufficiently developed for complete end-to-end testing. Check the limitations of the AI tool and add other resources to achieve a unified testing strategy.
- Check for security loopholes. During testing, QA engineers apply different third-party tools and algorithms. They can make the system vulnerable to security issues. Therefore, it is essential to attract cybersecurity experts and take special measures.
- Use high-quality datasets. Testers should perform their checks on clean data. So, make sure the algorithm generates accurate data.
FAQ
Should QA testing worry about AI replacing them?
As with any invention, there is something scary about the new things. But instead of panicking, it’s better to think of what AI is capable of. It automates such testing types as regression, load, or functional. It detects anomalies and malfunctions faster than humans do. It optimizes test suits and reduces maintenance effort. On the other hand, usability and exploratory testing can’t avoid human interference. AI doesn’t know how to evaluate users’ emotional feedback yet. It also fails to understand subjective requirements and edge cases that appear outside of the historical data. So, what testers should really worry about is how to improve their skills with AI and use the possibilities of AI tools to accelerate and optimize their routine tasks.
What are the AI tools used in testing?
AI-augmented testing tools include platforms like TestComplete, Katalon Studio, ACCELQ, Applitools, Testim, and LambdaTest KaneAI. They integrate such AI features as natural language processing, intelligent test generation, visual validation, and others.
What are the AI-powered software testing challenges?
The challenges of AI testing include dependency on quality data, failure to understand AI’s decisions, complex customization, risk of biased models, skill gaps, costs, etc. The list may be continued. However, it’s easy to address any challenges when taking the process of selecting appropriate AI tools seriously. Another way to avoid facing challenges is to partner with a reliable tech firm that will perform the whole cycle of testing services.