What will AI integration bring to the testers?
Enhanced Accuracy
Traditional testing still requires human resources for source and data analysis. But let`s just admit: even the most experienced QA engineers are prone to making mistakes, and that`s ok. The testers lose the focus on software QA and overlook some important defects because of extensive data. Before project stakeholders notice it, consumers pick up on the bugs. Usually, such situations are damaging to brand reputation and product positioning on the market.
And that`s were the AI and machine learning technologies come on the scene. They teach systems to learn source analysis and apply knowledge in the future. In such a way, AI tester comes up with more accurate results. The use of AI technologies for data analysis eliminates human error probability, shortens the time to run a test and find possible defects. As a result, the QA team is not overloaded with large data volumes to handle.
New Role for a Tester
AI testing of 2020 is a promising means of making the whole testing process more efficient. As it is going to take over the vast load of test execution, QA engineers will develop new skills. Working with AI requires to diversify competencies in AI testing, neuro-linguistic programming, math optimization, business intelligence, algorithmic analysis.
The experts of the World Quality Report assume the lack of such highly qualified professionals, although the practice of AI matures. Therefore, the companies will have to invest in the skills development of their QA teams. The recent issue of the World Quality Report suggests three emerging roles of QA engineers:
AI test experts. Along with the traditional testing skills, they are to build machine learning algorithms, understand math models, and work on natural language processing paradigms.
AI QA strategists. This team deals with the AI role in business processes. With the broad understanding of data flow, math optimization, and robotics techniques, they find ways to implement AI quality assurance practices into the entire business lifecycle.
Data scientists. As a part of the QA team, analysts filter data, use statistics, and conduct predictive analysis to build the needed models for AI-based QA strategy.
There are predictions that QA engineers will transform into the test automation team. They will take a supervisory role and teach AI to execute a range of tests. Vijay Shinde, the founder of Software Testing Help, believes AI can occupy nearly 70% of repetitive testing space. Anyway, humans will be in need to control test outcomes and focus on the remaining 30% of testing dealing with user scenario tests, for example. Moreover, test managers remain responsible for tooling, workflow modeling, and environment set up. While AI might be a smart assistant in running repetitive tests, it is a QA engineer who monitors the progress, considers test plan, takes control over QA strategy and objectives.
“Our intelligence is what makes us human, and AI is an extension of that quality.”
Yann LeCun, a French computer scientist
Predictive Analysis
The same data from World Quality Report estimates 64% of companies will imply AI into QA strategies to improve customer processes. The market demand in the IT field never stops growing, the businesses need to find a way to predict customers` needs and jump ahead of the competitors. This is a tough job for predictive analytics for software testing companies. AI and machine learning might assist in fast customer data analysis to clarify their preferences in new products and features.
Machine Learning in Testing
Machine learning (ML) is a technology based on pattern-recognition. The algorithms analyze tones of information and identify predictive patterns. This way transforms the automation testing approach, as ML doesn`t need a user interface to test. Much of the automated QA is a back-end-focused process.
UI tests. The use of ML bots might be helpful while working on end-user experience. Most of the modern apps have similar patterns in design, functionality, or interface. For example, you can easily find a shopping cart, product filter, and payment window in an online store. Bots can undergo training for a particular software area to run more test cases than regression testing would do. Image-based testing with the use of visual validation tools is a trendy pattern that ML recognizes. QA engineer may create a simple machine learning test that would automatically find visual defects in software.
APIs. ML testing efforts help to conduct an effective check-up of the API layer. The algorithms take over the analysis of test scripts, while a tester doesn`t get stuck with making a lot of API calls.
Strategy-orientation. Often, QA engineers run an entire test suite just because of small changes within the code. The use of ML tools enables to define a minimum number of tests needed to run to check the relevance of code modifications. It also analyses vulnerable software areas and current test coverage.
Challenges on the Way
Although AI seems to be a cornerstone for the future of QA, it is not that easy to make it real. There is a range of obstacles holding back the adoption of AI in software testing.
Ever-Big Data. To process the unstructured data requires additional human resources and financial support. Today, organizations hardly handle heavy data analysis and can`t proceed with the use of robotics or cognitive platforms.
AI Integration. For now, the companies fail to define the business areas to improve with the help of AI technologies. Moreover, most of the QA organizations can`t find 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 broadens testers` skills and expertise. It is only a question of time when this gap will be fulfilled. However, the need qualified professionals for AI testing in 2020 will increase, as more and more organizations experiment with “intelligent QA”.
“I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it can be our partner.”
Masayoshi Son, a SoftBank founder
AI-driven Test Automation Tools
Sure, the advent of AI is inevitable. But whatever the assumptions are, we believe that there is still plenty of time before AI takes over the whole testing process. Today, business and QA engineers talk a lot about the so-called “third wave of testing automation” because of innovative AI automation testing solutions. Let`s have a look at some of them:
Eggplant AI. This version uses intelligent algorithms to navigate software, predict the defects, and solve the issues with advanced data correlation. It enables automating any test automation engine, offers graphical analysis of test coverage and results.
Appvance. The tool offers a deep analysis of software via machine learning and produces “app blueprints” models applying cognitive generation. The blueprints promise to generate a lot of test cases in minutes. Apart from AI, Appvance implies Test Designer, a record-and-playback feature. Test Designer combines with data-driven tests, screenshot comparison, and DOM or AJAX auto capturing.
Applitools. A visual testing tool powered by AI. An intelligent cognitive vision helps to outline the expected design of an app. Applitools offers a visual comparison algorithm to detect and report any differences found in the user interface of an app. The tool is good at running automated tests and find thousands of interface inconsistencies in minutes.
Test.ai. It identifies the screens and elements of software and drives an app to execute test cases. The tool adjusts to any changes of an element and even identifies the pieces that require manual corrections.
Testsigma. AI-driven tool for continuous automated testing. It uses natural language testing processing to write quality auto tests. Testsigma identifies the relevant test cases for the test run and prevents unexpected test failures.
Thanks to AI a team of testers can move beyond the traditional route of manual testing models and progressively move forward toward an automated and precision-based continuous testing process. The QA experts have to perceive AI bot as a useful addition, which will reduce the workload and make a tester’s life much easier.
