AI in Quality Assurance: Importance of Automation in Artificial Intelligence

  • By
  • January 4, 2021
  • Software Testing

Artificial Intelligence (AI) is revolutionizing the globe! This technology attracts massive capital investment and genuine interest from renowned business owners due to its powerful benefits. Businesses are backing up to integrate artificial intelligence (AI)to simplify operations and reduce costs. Artificial Intelligence helps share data efficiently, target marketing demographics personally, predict customer behaviour, suggest products, detect frauds, and provide valuable customer service.

If you are still on the crossroads of AI is applicability, here are some figures that will shock you: –

  • 62% of companies say the burden to minimize costs will compel them to make use of AI.
  • 61% of business officials say AI helps them identify business opportunities that they would otherwise not notice.

Using AI in quality assurance (QA) caters to the unique challenges the department faces. 

 

Benefits of Using AI Technology in Testing & Quality Control (QC)

  • Expedited Timelines 

There are many options where our team of developers speed up the app development process. Incorporating disordering in the testing process is one of them. In order to go through thousands of lines of codes, AI will be able to sorting the log files, scan the codes, and detect errors within seconds. Additionally, AI lacks the burnout syndrome and thus gives better and more accurate results. 

Also, Artificial Intelligence can evolve with the code changes. It can adapt and verify new functions and can be programmed to decide if something is a new feature or a bug arising out of code change. 

  • Well-Researched Build Release 

By using Artificial Intelligence in QA, it becomes possible to examine similar apps and determine what come up with their success in the market. Based upon  the understanding of market requirements, new test cases can be made for ensuring that the app does not break when it comes to achieving specific goals. 

  • Effortless Test Planning 

Currently, a good amount of QA expert’s time goes into planning of test case scenarios which will get them application launch confidence. The same process as to be applied every time when a new version is released in the market. 

AI test automation tools can help testers to observe the application by slinking through every screen and generating, executing test case scenarios for them. Thus it saving on the planning time and cost. 

  • Expanded Role of a Tester

While AI comes in the picture, the teams of QA engineers will find themselves learning new skills. They will need to up-date their skills like neuro-linguistic programming, algorithmic analysis , business intelligence and math optimization. 

Job title wise, we can expect these names coming currently– 

  • AI QA Strategists 
  • AI Test Experts
  • Data Scientists

 

  • Speedy Timelines

AI has remarkably improved the testing method by grouping log files, checking the code, and recognizing mistakes in seconds. Furthermore, AI does not tire out and produces enhanced results. In addition to its inefficiency, Artificial Intelligence is a self-learns, and advances with the evolving code. AI can also awareness about, when a code change is a new element or a bug because of the new change.

  • Well-Researched Build Release

Artificial Intelligence  helps businesses assess their competitor’s application to understand its USPs that contributed to their success. As per demanding of the market requirements, specific cases addressing those needs can be made to ensure smooth functioning. Adhering to these requirements helps businesses achieve success.

  • Effortless Test Planning

Software QA professionals spend a lot of time preparing test case scenarios before launching the software. Adding new versions and code changes demand a similar rigor from the QA specialists. Artificial Intelligence (AI)automation tools examine the app through all its screens, produce apt test case scenarios, and saves valuable preparation time.

The Six Levels of AI Testing You MUST know!

 

The words were wise, but for software testing and QA they represent a whole new image of AI implementation. No technology will ever replace its creator and the control is always within the reach!

Now a days, AI is going to be an ultimate support towards high computing, valuable performance, agility and time balance with more accessibility as well as accountability.

Businesses are putting more and more efforts to test as well as maintain their software product’s quality and that’s why you must learn the 6 stages of AI-Based Testing:

 

Stage Zero: ZERO AUTONOMY 

This is a initial stage where writing code is a repetitive process, the addition of anything even as small as adding a field means adding it to a test. More the tests, greater are the instances of failure to ensure the applications functionality as a complete product. To solve this issue tester, need to ensure that all failed tests are checked for a bug.

 

Stage One: VISUAL ASSISTANCE FOR AI

On the Second Stage, more the better an AI can view the apps more the autonomous QA process will become. Along with visuals, AI should look at the page’s Document Object Model (DOM), Now the tests can be written automatically which otherwise had to be written manually. Algorithms need to be in place which can differentiate actual changes from expected or not. Artificial Intelligence (AI) can’t only write test cases but also check them and notify should the test fail which can later be checked manually if the failure is real or because of change in software.

 

Stage Two: PARTIAL AUTOMATION

Up till this stage, QA engineers ignore the time-taking aspects of writing checks, also visual and document object model elements can be checked. AI have a capability to understand the differences in an application just as a regular user would understand, thus AI can group the changes from different pages.

 

Stage Three: CONDITIONAL AUTOMATION

Up to the previous stage, human mediation is needed to make any change or to correct failed test cases. Now, AI now can apply machine learning (ML) techniques, examine visual elements and use the rule of design to analyse if the UI is right or not. On this third level, AI can evaluate pages simply by the rule of designs and understanding the data.

 

Stage Four: HIGH-LEVEL AUTOMATION

Human intervention was still needed to drive the tests, at this stage AI can take over and test an app significantly just like a user would. Understanding of user flow and the pages will becomes simple as AI can visualize the interaction it has learned over time. Learning a new technique will be useful to drive test script automatically.

 

Stage Five: COMPLETE AUTOMATION

At this last stage, AI can directly communicate with the product in-charge, understand the applications and drive the test without any human intervention. Visual testing, API testing and automated testing can currently be performed by AI. Also this is an ideal combination of scalable professional resources at the right place and right time, at a ruthless price. Our qualified team with experienced software testers can check your product on various parameters like usability, compatibility, functionality, security and scalability.

 

The Popular AI Testing Framework and Platforms 

Although slow but Quality Assurance has been making entrance and prevalence in the third wave of automation with the help of AI-driven quality assurance platforms. 

Below are some of the most top AI test automation tools operative in the market today

 

 

 

  1.   Eggplant AI. 

Eggplant makes use of intelligent algorithms for navigating software, predicting the many defects, and solving the challenges that are helpful for advanced data correlation. Eggplant AI also enables automating of test automation engines and provides graphical analysis of test coverage and outcomes.

  1. Appvance. 

The platform provides software’s deep analysis through machine learning (ML) and delivers “app blueprints” models which apply cognitive generation. These blueprints hold the capability to develop multiple test cases with in a few minutes. It also comes with a Test Designer functionality that can be combined with screenshot comparison, data driven tests (DDT), and  DOM auto capturing. 

 

  1. Testim.io

Testim.io makes use of machine learning for authoring, execution, and maintenance of the automated tests. This tool focuses on functional, end-to-end (E2E) and user interface testing. The platform continuously becomes smarter and create stability of its test suites increases with more runs.

  1. Testsigma.

It is one of the most commonly used tool for AI-driven tools for continuous automated testing. This platform makes use of natural language testing processing for writing auto test cases. Testsigma also identifies relevant test cases for test run and saves unexpected test failures.

 

Challenges In Quality Assurance Processes and AI Combination

Even if the future of AI and quality management belongs together there are many obstacles that it will have to cross for being 100% adopted in software testing. Here are some of those challenges – 

  • Data management is not a mainstream process 

Processing terabytes of unstructured data requires a lot of manpower as well as financial support. Today, there are only a handful of businesses which have the capacity to handle heavy data, let alone analyse it and prepare it for ML. 

  • Lack of AI expertise 

Artificial Intelligence (AI), as a skill, is still at a growing stage. This makes finding the expertise who can take control of an intricate process like application testing is extremely difficult. Although you manage to find the resource, it would cost a lot for a start-up level business to handle. 

Role of Artificial Intelligence in Software Testing

Here are all the important functions of artificial intelligence in software quality assurance (QA):

 

 

  • Builds Test Cases and Finds Bugs

Its perceptive abilities make artificial intelligence build better test cases by exploring all possible ways. Using powerful combinations and visualization techniques, artificial intelligence builds superior test cases that cover all the test scenarios. The founder of Software Testing Help believes artificial intelligence can help with 70% of the repetitive testing space.


  • Finds Developer’s Quirks and Historical Bug Patterns

AI speedily recognizes the typical code writing style of each coder. This analysis helps recognize what common issues the developers face and assists them to avoid such mistakes. Gently, AI creates a lot of historical bug patterns for quicker bug detection.


  • Predicts how Customers will React while Release Features

AI having a high-speed analytical processing gathers deeper insights into customer behaviours. These analytics helps assess how the customer is likely to react to the addition of a new feature in the application. These types of predictive analyses help top-level management make better decision-making.

  •  Conclusion

Artificial Intelligence in an undeniable reality of the 21st century. Whoever jumps on the AI bandwagon earlier has a massive advantage. However, simply throwing money at AI without considering reason why you specifically need it can be extremely loss. So, I hope you specify your reasons for implementing Artificial Intelligence before actually going through with it.

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