Published: 16 Mar 2021
AI in Software Testing – Benefits, Approaches, Tools to Look in 2022
Last Updated: 18 Apr 2022
Artificial Intelligence (AI) is the latest technology to be leveraged across all industries and domains. AI continues to make its way into domains previously reserved for human skills and has been enabling significantly dominant results. On a similar note, AI is being leveraged in the field of software testing also to ease the automation testing process and deliver more quality outcomes.
Content 1. What is AI-based Testing? 2. How has AI evolved in software testing? 3. Some of the benefits of leveraging AI in software testing 4. What are the 4 key AI-driven testing approaches? 5. What are the 4 main categories of AI-driven testing tools? 5.1 Differential tools 5.2 Visual AI testing tools 5.3 Declarative tools 5.4 Self-healing tools 6. Conclusion
According to the World Quality Report 2019-2020, it is stated that AI-based testing is on the rise, and to make testing smarter, more effective, and more efficient, organizations are adopting AI-based tooling and processes in software testing. Typically, the application of AI in software testing will make the entire testing process faster, clearer, easier, and budgeted. Therefore, AI-based testing will provide a strategic platform where software testers can leverage AI and take the testing process to a new level and thus deliver more quality results to businesses.
Test automation tools have been supporting the continuous testing process for many years. But, now the need for more effective tools and techniques such as embedding AI in software and QA testing has become a mandate for delivering quality software and for ensuring superior customer experience.
AI-based testing is a software testing technique in which AI and Machine Learning (ML) algorithms are used to effectively test a software product. The objective of AI-based testing is to make the testing process smarter and highly effective. With the inclusion of AI and ML in testing, logical reasoning and problem-solving methods can be applied to improve the overall testing process. Further, in this testing method, AI testing tools are used to execute the tests that use data and algorithms to design and perform the tests without any human intervention.
The paradigm of software testing has evolved significantly over the past two decades. Right from manual testing to automation testing, where selenium is considered to be one of the finest test automation tools, the testing journey has been quite encouraging. However, in today’s fast-paced IT world, the domain of software testing has to come up with innovative and well-researched testing methodologies. For this purpose, the dawn of AI-based testing has come up and has been proving very impactful.
AI algorithms can completely mimic human intelligence, and ML allows computers to learn automatically without any human intervention. Interestingly, AI and ML involve the development of unique and specific algorithms that can access data, learn from that data by extracting patterns to make decisions, and these predictions are to be used in software testing effectively.
Moreover, enterprises are rushing towards tools that can leverage AI and ML algorithms and can be used for testing the software effectively. It has also been seen that businesses get many benefits from AI-based testing as it will enable faster and continuous testing, complete automation without any human intervention, and also enables quicker ROI.
AI has pattern recognition and image recognition capabilities that together help to detect visual bugs by performing visual testing on applications. It helps to make sure that all the visual elements are engaging and can function properly. AI can recognize dynamic UI controls irrespective of their size, shape and analyses them at a pixel level.
In the manual testing method, the chances of human-prone errors are high, especially in situations of repetitive tasks. Automation testing helps in removing these human-prone errors. But, with the advent of AI in automation testing, repetitive tasks are handled more effectively and the results are recorded more accurately. Thus, AI helps in removing the minute chances of errors and improves the overall accuracy of tests.
AI in testing increases the test coverage as it can check the file contents, data tables, memories, and internal program states seamlessly. It also helps to determine if the program works as expected and delivers effective test coverage.
Software tests need to be repeated whenever there is an instance of change being made in the source code. Manually this becomes time-consuming and takes a lot of effort from testers. But, with AI-driven tests, repetitive tasks are handled properly, quickly, and efficiently.
AI-driven tests support continuous testing, and thus products are released faster which helps businesses go early-to-market.
AI in testing helps in early and fast bug identification, which ultimately reduces the defects and makes the product bug-free, and reliable for end-users.
Differential testing – Differences are classified and application versions over each build are compared in this type of testing.
Visual testing – Visual testing is a software testing technique in which the look and feel of an application are tested by leveraging image-based learning and screen comparisons.
Declarative testing – It aims at specifying the intent of the test in a natural or domain-specific language. The system decides how to perform the test
Self-healing automation –Element selection in tests is auto-corrected when the UI changes.
These tools leverage AI and ML algorithms to identify code-related issues, security vulnerabilities, regressions, etc. This is achieved through code scanning, unit test automation, etc. Some of the tools under this category are:
With the ever-growing number of platforms that vary in screen sizes and have different configurations, it has become a tedious task for test engineers and developers to effectively test the UI layer. Also, the UI layer experiences constant changes from time-to-time as businesses wish to provide a better user experience. Therefore, today there is a dire need for visual AI testing tools that effectively test all variations of these UI layers. Some of the tools that fall under this category are:
These tools aim to enhance test automation productivity and stability. These tools leverage AI and ML and have significant abilities related to Robotic Process Automation ( RPA), Natural Language Processing (NLP), Model-based Test Automation (MBTA), and Autonomous Testing Methods (AT). The main aim of these methods is to eliminate tedious, error-prone, repetitive tasks through smart automation. Some of the tools that fall under this category are:
In automation tests, the problem of flakiness, reliability, and maintenance issues persist, and this is one of the main reasons why AI and ML have been introduced in test automation. To overcome these problems, self-healing tools have been developed that are mostly based on a record and playback mechanism, wherein the main ML engine resides in the self-healing of the recorded scripts. Some of the tools that fall under this category are:
AI-driven test automation tools not only support DevOps practices but also bring in human-like decision-making abilities which ultimately helps in releasing high-quality software in less time. Leverage next-gen AI-based testing services by next-gen testing services provider to get faster and quality releases with more efficiency and accuracy.