By definition, AI is a system, built through coding, business rules, and increasingly self-learning capabilities, that can supplement human cognition and activities and interacts with humans naturally, and also understand the environment, solve human problems, and perform human tasks
Data mining and AI has been revolutionizing every industry of late. AI minimizes human involvement in critical tasks such as complex decision making. It makes tedious and time-consuming tasks much easier. A recent example of AI implementation is Google which used AI-powered medical equipment in hospitals which predicts a major medical event 48 hours before it’s occurrence which can be crucial in saving lives. AI has yielded benefits even far beyond healthcare. However, not enough advantages of it have been taken for software quality enhancement. But the time is not far when AI will play a major role in this area as well.
Since more industries are moving towards automation, they are trying to get their hands on AI-based apps. This makes testing the apps for automation a business-critical activity. This gap is being identified and software testing companies, especially test management tools are being equipped for the future.
Machine learning helps your organization answer the following questions regarding test management:
- Have we run the most important test cases at the start of the cycle?
- How critical are the open defects in the software we’re about to ship?
- When QA teams say that it is 70% done, have they covered the most important test cases that were a problem over the past several releases?
- Are we making adjustments with proactive steps?
- How many defects do we have? How many test cases have been written and executed? What’s our defect closure rate? What does our complexity look like?
According to the 2016-17 World Quality Report, “the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence,”. Organizations are pursuing digital transformation and AI can be a major driver. According to Gartner, AI technologies will be in every new software product by 2020.
AI’s interactions with the system multiply result you’d have with manual testing. AI-based testing uses data in existing QA systems such as defects, resolutions, source code repo, test cases, logging, etc. to help identify problem areas in the product. How will AI testing affect tester? Testing companies would require tester with a different skill set to build and maintain AI-based test suites that test AI-based products. The job requirements would include more focus on data science skills, and test engineers would be required to understand some deep learning principles.
AI requires data, computing power, and algorithms. Now, big data and colossal computing power have made AI such a distinct reality that CIOs rank it as their top strategic investment. AI is undeniably valuable—and necessary—for transforming testing to meet new expectations such as real-time risk assessment.
The following are the reasons why we believe AI will revolutionize the software testing industry.
1. Accelerate Manual Testing and Overall Processes
Manually typing tons of test lines such as, “click here” and “check that” puzzles developers’ attention. Complete test passing may take several days, and sometimes several weeks. With AI, coding becomes much easier and faster. AI can handle sorting through log files and so it will save time and enhance correctness in the program tremendously.
2. Automate Testing Process
Self-learning is what makes artificial intelligence so “intelligent”. Application and code changes do not pose problems for AI bots since they continue to adapt and learn to find new changes in the application functions themselves. When the AI identifies modifications, it automatically estimates them to decide whether this is new functionality or defects of a new release.
3. Eliminate More Bugs
Bugs often remain unnoticed even if the tester does everything right. But with AI, bug detection can get much faster and easier by addressing all the how what, and when questions in a matter of minutes or seconds. Testers can use this information to decide whether coding changes are required to prevent program errors or they simply need to apply some other approaches.
4. Forecast Client Requirements
Clients’ requirements can be easily forecasted by analyzing their data with the help of machine learning. This will result in greater customer satisfaction and ensure the delivery or product/service as the customer desires.
Conclusion
The significance of AI in today’s world can not be ignored. It has revolutionized industries and is yet to achieve its full potential in software testing, especially test management. In test management tools, AI can address questions regarding speed, complexity, and defect count in a much better way. With tons of data at its disposal, AI-based test management uses machine learning to find solutions for the testing problems. Although it’s still just the tip of the iceberg, benefits it is yielding today give us a glimpse of how fast and “intelligent” tomorrow will be. Testing processes will be faster, more efficient, and more effective. And so AI will take control of nearly everything.
