AI in Software Testing
AI needs testing, testing needs AI.
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.
Manual testing involves a lot of work and can be time-consuming. At the end of this article, you will find out that AI is the sugar in software testing.
Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals.
AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Responsible AI in software testing aims to make testing smarter, unbiased, monitorable, and more efficient. AI and machine learning apply reasoning and problem solving to automate and improve testing. AI can perform tasks better than humans. Particularly when it comes to repetitive, detail-oriented tasks like testing, so teams can channel their energy on more complex tasks, like creating innovative new features.
AI-Based Testing
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.
Extract from the World Quality Report 2019–2020 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 budget. 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.
With AI, human intelligence is automated to have smarter programs and machines, to develop useful techniques for solving difficult problems, and to understand human intelligence better.
The computer system due to AI now:
· Think humanly.
· Act humanly.
· Think rationally.
· Act rationally.
AI has been and has still been applied in various aspects of life like Mathematics, economics, neuroscience, Linguistics, Psychology, philosophy and so applying it in testing makes a lot of difference and upgrades in software testing.
We have a lot of systems and machines been developed with AI in our world today. As AI continues to permeate our world, it is becoming more and more critical to validate that these types of systems are functional, safe, secure, performant, available, and resilient, and user friendly. In other words, AI needs testing. Unfortunately, we have not seen many advances in testing AI-based systems.
On the bright side, researchers and practitioners are recognizing the potential for AI and Machine Learning (ML) to bridge the gap between human and machine-driven testing capabilities. As a result, several organizations are developing AI-powered automated testing tools. So, there is movement from Manual to Automation testing using Selenium and other open-source tools to AI-based testing.
AI needs testing, testing needs AI.
The use of artificial intelligence (AI) in test automation is the latest trend in quality assurance. Testing in general, and test automation, seems to have caught the “everything’s better with AI” bug. Since AI, machine learning, and neural networks are the hottest thing right now, it is perhaps inevitable that AI would find its way into test automation somehow.
Research has it that since 2014, there has been a spike in the number of vendors offering AI-driven test automation services. Many of these tool vendors are start-up companies targeting system-level testing of mobile applications, and the subject is generating some much-needed buzz in the industry.
Although some aspects of AI for Software Testing are still not receiving enough attention, in the last decade we have seen it emerge as a discipline centered at the intersection of three areas:
· AI-driven testing — developing AI tools to test software.
· Testing AI systems — devising methods to test AI systems.
· Self-testing systems — designing software that is capable of self-testing and self-healing.
How has AI evolved in software testing?
The paradigm of software testing has evolved significantly over the past two decades. Right from manual testing to automation testing, where selenium is 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 must 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 enables quick ROI.
Some of the benefits of leveraging AI in software testing
· Reduces defects.
· Better test coverage
· Faster time to market
· Good at detail-oriented jobs.
· Reduced time for data-heavy tasks.
· Delivers consistent results.
· AI-powered virtual agents are always available.
· Faster completion of project in Agile method
· Improves accuracy in software testing.
Preparing for The Future
In automation: When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of
enterprise jobs, enabling RPA’s tactical bots to pass along intelligence from AI and respond to process changes.
So, what can testers do to prepare for a future in which AI tests software? The initial step is to determine whether you are interested in designing and developing AI-driven systems or being an end-user of these tools.
Vendors are likely to provide easy-to-use interfaces and APIs for using AI-driven testing features and customizing pre-trained AI models.
AI is already altering the landscape of testing. And while we don’t know exactly what the future of software testing holds, we can prepare for it by stabilizing and scaling test automation and getting in tune with AI technologies
The application of AI in software testing tools is focused on making the software development lifecycle easier. Through the application of reasoning, problem-solving, and, in some cases, machine learning, AI can be used to help automate and reduce the number of tedious tasks in development and testing.
“Don’t test automation tools do this already?” you might ask.
And the answer is of course, “Yes! They do!” …but they have limitations.
We are employing AI to deal with those limitations, to enable software test automation tools to provide even more value to developers and testers. The value of AI comes from reducing the direct involvement of the developer or tester in the most tedious tasks. (Human intelligence is still very much needed in applying business logic, etc.)
I recently read about a software called “Parasoft” a test automation software that works as an AI-enabled bot that can review the current state of test status, recent code changes, code coverage, and other metrics, decide which tests to run, and then run them? Bringing in decision-making that is based on changing data is an example of applying AI. The software is effectively able to replace the developer/tester in the decision-making process.
The world is moving at a fast pace and as a tester, if you do not want to be left out in the testing world you need to move too. AI automation testing is the future of software testing.
Reference — Perfecto.io
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