AI in Quality Assurance: Smart Assistance for Software Testing Engineers | Qulix Systems

AI in Quality Assurance: Smart Assistance for Software Testing Engineers

Apr 12, 2018

blog

The QA and software testing challenges have never been so wide-ranging. Apps interact with each other via APIs, constantly growing in complexity.

AI and Machine Learning will become the key solution to cope with those problems.

How will it change the future of software testing? And, what will happen to test engineer´s profession?

AI possible uses

The potential of AI technology for software testing is enormous.

First of all, AI can be used to optimize test management as well as generate test cases automatically. It will allow reducing testing efforts and increasing efficiency. The result is a ´better, faster, cheaper´ testing. In fact, usually, companies are forced to choose just two of these qualities.

Secondly, AI can be applied to create test code and pseudo code automatically from user story acceptance criteria.

And, finally, AI for codeless test automation to create and perform tests on web and mobile apps without writing a code.

Much will be changed in the software testing area with the entry of AI. However, the technology has to be considered as a smart assistance, not as a threat to the job.

Smart assistance

Test automation is currently a must. In reality, test execution is the only element which has been actually automated.

AI and predictive analytics will be used to enable a real test automation: e.g., providing recommendations which test to carry out, continuous learning, predicting business effects, so problems can be fixed before they appear.

Software engineers will continue playing an important role. During test performance, the software test specialists should control the test progress, and take actions, if necessary.

Hence, the technology will enhance the testing processes by enabling teams to conduct tests faster and smarter.

Сhallenges

The technology in the field of test automation still has some difficulties to overcome. Creating an AI-based app for testing may provide a range of possible challenges, e.g.:

  • Continuous improvement of the required algorithms
  • Gathering input data for constant bots training
  • Dealing with bots behavior on basis of input data

What does it all mean for testers?

A software working properly does not automatically mean that it is the product a user was expecting. That´s why testing process still demands the involvement of human specialists.

AI will help software testing engineers to do less mechanical work required for implementation, execution as well as analysis of test results. However, software testers will be asked to make active decisions according to obtained insights.

Besides, test engineers will be expected to have a set of particular knowledges in the field of AI technology to be able to manage AI-based tests. It means, specialists applying for software testing engineer positions will be required to demonstrate their data science skills and knowledge of deep learning core principles.

Find out more about Qulix QA Services

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