Software testing conducted by human resources still has its value, although Artificial Intelligence (AI) is a promising method to make the process simpler, faster, clearer. Someday, the emerging technology of AI may drive software testers to begin looking for a new job elsewhere. But don`t get tripped up with such forecasts. Procedures to implement AI and machine learning are far from perfect organizations still have lots of challenges to come through. However, one thing is clear: the use of AI by QA professionals would upgrade the whole testing process, improve testers` professional skills, and provide business growth.
What will AI integration bring to the testers?
Traditional testing still needs human resources for source and data analysis. But let`s just agree: even the most experienced QA engineers are likely to performing mistakes, and that`s ok.
The testers lose the focus on software QA and neglect some major defects because of extensive data. Before project stakeholders see it, customers pick up on the bugs. Normally, such situations are damaging to brand reputation and product positioning on the market.
And that`s where the AI and machine learning technologies appear on the scene. They teach systems to discover source analysis and utilize knowledge in the future. In such a way, the AI tester comes up with more specific results. The application of AI technologies for data analysis reduces human error probability, reduces the time to run a test, and detects possible errors. As a result, the QA team is not loaded with large data volumes to manage.
New Role for a Tester
AI testing of 2020 is a promising means of getting the whole testing process more effective. As it is going to take over a huge load of test execution, QA engineers will acquire new skills. Working with AI needs diversifying competencies in AI testing, neuro-linguistic programming, business intelligence, math optimization, algorithmic analysis.
The experts of the World Quality Report consider the need for such highly qualified professionals, although the use of AI matures. Therefore, the organizations will have to spend on the skills development of their QA teams. The recent issue of the World Quality Report recommends three emerging roles of QA engineers:
- AI test experts. Along with the regular testing skills, they are to develop machine learning algorithms, learn math models, and act on natural language processing paradigms
- AI QA strategists. This team trades with the AI role in business methods. With a broad knowledge of data flow, math optimization, and robotics methods, they find ways to implement AI quality assurance methods into the complete business lifecycle.
- Data scientists. As a part of the QA team, analysts filter data, use statistics, and manage predictive analysis to create the needed models for AI-based QA procedures.
There are predictions that QA engineers will change into the test automation team. They will take a supervisory role and teach AI to perform a range of tests.
The same data from World Quality Report predicts 64% of organizations will imply AI into QA strategies to enhance customer processes. The market interest in the IT field never stops growing, the businesses require to discover a way to predict customers` requirements and jump ahead of the competitors. This is a difficult job for predictive analytics for software testing organizations. AI and machine learning might help in fast consumer data analysis to explain their preferences in new products and features.
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Machine Learning in Testing
Machine learning (ML) is a technology depending on pattern recognition. The algorithms analyze tones of information and recognize predictive patterns. This way changes the automation testing method, as ML doesn`t require a user interface to test. Much of the automated QA is a back-end-focused method.
UI tests. The use of ML bots might be helpful while operating on end-user experience. Most modern apps have related patterns in design, functionality, or interface. For example, you can simply discover a shopping cart, product filter, and payment window in an online store. Bots can experience training for a specific software area to run more test cases than regression testing would do. Image-based testing with the application of visual validation tools is a popular pattern that ML identifies. QA engineers may build a simple machine learning test that would automatically detect visual defects in software.
APIs. ML testing efforts help to manage an effective check-up of the API layer. The algorithms take over the review of test scripts, while a tester doesn`t get stuck with performing a lot of API calls.
Strategy-orientation. Often, QA engineers manage an entire test suite just because of small changes within the code. The use of ML tools allows defining a minimum number of tests required to run to verify the relevance of code modifications. It also examines vulnerable software areas and current test coverage.
Challenges on the Way
Although AI appears to be a cornerstone for the future of QA, it is not that simple to make it real. There is a variety of obstacles holding back the adoption of AI in software testing.
Ever-Big Data. To process the unstructured data needs additional human resources and financial support. Today, companies simply handle heavy data analysis and can`t proceed with the application of robotics or cognitive platforms.
AI Integration. For now, the organizations fail to describe the business areas to enhance with the help of AI technologies. Moreover, most QA companies can`t discover a way to approach AI technologies. The teams need to build up expertise first in order to emerge AI and business lifecycle.
Lack of AI knowledge. As mentioned earlier, intelligent testing increases testers` skills and expertise. It is only a question of time when this way will be fulfilled. However, the need for qualified professionals for AI testing in 2020 will grow, as more and more organizations experiment with “intelligent QA”.
AI-driven Test Automation Tools
Sure, the advent of AI is necessary. But whatever the presumptions are, we believe that there is still a lot of time before AI takes over the whole testing method. Today, business and QA engineers discuss a lot about the so-called “third wave of testing automation” because of innovative AI automation testing solutions. Let`s have a glimpse at some of them:
Eggplant AI. This version utilizes intelligent algorithms to navigate the software, foretell the defects, and resolve the issues with advanced data correlation. It allows automating any test automation engine, allows graphical analysis of test coverage and results.
Appvance. The tool allows a deep analysis of software via machine learning and produces “app blueprints” models employing cognitive generation. The blueprints agree to create a lot of test cases in minutes. Apart from AI, Appvance indicates Test Designer, a record-and-playback feature. Test Designer mixes with data-driven tests, screenshot comparison, and DOM or AJAX auto capturing.
Applitools. A visual testing tool powered by AI. An intelligent cognitive vision serves to describe the expected design of an app. Applitools a visual comparison algorithm to discover and report any differences located in the user interface of an app. The tool is good at operating automated tests and find thousands of interface inconsistencies in minutes.
Thanks to AI a team of testers can move past the traditional route of manual testing models and progressively move forward near an automated and precision-based continuous testing method. The QA experts have to see AI bot as a useful addition, which will decrease the workload and make a tester’s life much simpler.
Need to implement AI testing in your project? Think no more! TestUnity provides the testing services on-demand, serves with projects of any scale, and is ready to start with a few days’ advance notice. Choose to team up with a QA services provider like TestUnity. Our team of testing experts specializes in QA and has years of experience implementing tests with different testing software. Get in touch with a TestUnity expert today.
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