With enhanced Machine learning and analytics techniques, AI has emerged as an intelligent solution in every domain from manufacturing to the healthcare industry. Robotics, Predictive Analytics, Big data Mining has leveraged a new dimension in testing. Intelligent systems can make sense of the vast quantities of data. They actually make recommendations for you about what to do and look at your results and tell you how to refine what you are doing. It radically improves your time to market, the value of your human resources and can improve profitability if you do it right.

Artificial intelligence is molding every aspect of our life. With the rapid use of data analytics and the increasing exploration of DevOps, Artificial Intelligence in software testing is now taking a new turn in the software world. The use of Artificial intelligence in software testing is not new. With the increasing use of the agile model in development, we can see a new paradigm with more intense testing to accelerate market time.

Related post – Why you should add automation testing to your skill set

Artificial intelligence in Software testing can overcome the limitations of traditional testing

1. In traditional testing, testers are supposed to find bugs and any new features not to affect them. It measures quite an amount of time, whereas using artificial intelligence in software testing, you can feed an AI bot with a decision algorithm to decide between a bug/feature.

2. Manual testing scripts need additional management of resources like machines to run and many person-hours to execute. As a result, testing can’t keep pace with the ongoing development, which is a prime need in today’s agile development model. Artificial intelligence in software testing can easily overcome these shortfalls with little maintenance.

3. Most of the testing is the repetition of the same test scenarios. In traditional testing, you need to spend the required person-hours and cost for the same, which an AI bot can save.

4. You can get optimized test cases through AI bots, eliminating similar test cases, and ultimately saving time.

5. Traditional test case generation focuses on validating requirements. Simultaneously, many considerable scenarios can be extracted from project documentation like SOW, defect logs, test results, production bugs, etc. Analytics-based AI bots can offer QA based on log analytics, defect analytics, and customer-centric analytics like social media feed. Moreover, artificial intelligence in software testing can identify more complex scenarios from the requirement traceability matrix programmed properly.

Real-time scenarios where artificial intelligence in software testing have improved performance

Facebook’s Infer: It was a static code analyzer using AI bots that identify bugs in the mobile code of Facebook messenger, Facebook app, and Instagram. The Infer helps to move users faster in mobile, removing bugs and saving tons of human hours and money.

TCS’s 360-degree assurance: This AI-based tool is also IP based and analyzes data collected from the different source across the development in an agile environment and automates the testing process. This IP way of DevOps testing helps to reduce deployment time from 6 weeks to 4 weeks. The AI bot is designed in such a way that it can analyze the rationale of generating series of events out of a defect in production.

Accenture’s touch-less testing platform: Developed by using AI and analytics, testers can integrate tools from leading marketers like Tableau, Work soft, and many more to bring new high-quality enterprise solutions.

Key areas that AI can leverage improvement in testing domain

Area 1—Performance:

With the numbers of similar apps in the market, improved app performance is the key area to sustain. In human lead testing, you cannot go beyond the measurement limit, which is usually defined by your production environment, SDKs, the raw data. And ultimately, the performance regressions are only caught after a long testing procedure.

Solution: Artificial intelligence in software can catch performance easily for every module and with every new build. It gives you a holistic picture of the slowest part of your developed application.

Area 2—Release time:

With enhanced customer demands and an agile environment, manual testing and automation testing are not the final answer. When manual testing is time-consuming, automation testing is quite expensive and sometimes breaks unexpectedly.

Solution: As mentioned earlier, AI bots can generate huge test coverage of most test teams; the bots could automatically discover new features and test new behaviors. In case it is beyond the limit of the bots, they send before and after pictures to the team to decide whether that is a bug or feature.

Area 3: Save money, time and resource:

Automation may be an answer for a faster test process; it needs manual effort and cost to set up an automated infrastructure.

Solution: Although AI-powered bots are also human-made, the cost is one time, hence less expensive. Its human’s field to implement their creativity and judgment into a machine to provide from test coverage to predictable analysis within minutes through AI testing.

Conclusion:

Like every new invention, every technology has its flip side. As AI bots have the intellectual capability, human control is limited in making a judgment once implemented. For example, during the Sydney cafe siege in 2014, when people called UBER taxis to take help, the application surged the price as demand wise high, although it was a sensitive crisis because the algorithm and not heart drove it! So, the best result may be the harmony between AI and humans, showing up a new, improved solution in the testing domain. Also, there is no need to be scared from a career perspective as testers only need to monitor the bots and results. Yes, what would matter is constant learning and specialization in these areas.