Implementing Artificial Intelligence in Software Testing

Artificial Intelligence (AI) has become a buzz in the present day scenario. It’s a fact that it’s been around since the 1970s or even maybe earlier. Most Technology Giants in 2017 such as Facebook, Amazon, Google, and Microsoft are investing billions in AI initiatives.

Microsoft at Build 2017 announced Microsoft Cognitive Services to enable developers to integrate Artificial Intelligence into their developing applications. It looks like Artificial Intelligence is just going to keep growing. And there is a role for it to play in software testing, too. There are several disciplines in the science of artificial intelligence as listed below:
• Machine Learning
• Expert Systems
• Neural Networks
• Optical Character Recognition
• Natural Language Processing

Some among these are mainstream than others. There are distinct handy open-source libraries to provide heavy lifting and pair with on-demand cloud computing that makes them even stronger.
However, coming to the case of testing you need to define your goals like
• What do you want to perform with Artificial Intelligence in testing scenarios?
• Do you want to create automatic test scenarios?
• Is your requirement is generating test code?
• Are you looking for artifacts validation that is hard to process by conventional computing?
• Do you want to compare images?
• Are you looking for something that is out of order?

With Agile and DevOps methodologies adoption, Software development and testing continues to evolve in steps and will continue to evolve in AI era too.

Artificial Intelligence in Software Testing

Both Artificial Intelligence and Machine Learning are centered on software training to gain knowledge of input data versus output data today. It is a similar action to the manual testing activities today. We type input into the field and look for the expected output.

With AI, the machine performs testing. It comes up with distinct test variations and runs automatically without human supervision. Even it can handle code changes and UI that QA professionals handled previously.
Testing tools even without Artificial Intelligence have evolved. Tools today help testers create, organize and prioritize test cases. Managing tests and their outcomes effectively and remediating defects has become essential to provide developers the needed feedback. Here we have to consider two major things: scalability and silos.


Testing data is continuously growing, and spreadsheets can’t report on test data trends alone or rerun tests. At this point, your organization needs more than normal from testing and Testers should make sure that they are capable of reusing these tools and supports quick tests.


No longer are the product development phases isolated. Each product development phase today has become an integral part of the development lifecycle. The application lifecycle management tools have to support the entire development process at every point such as requirements gathering, tests management, traceability performance, etc.
The testing surface areas have never been so broad in today’s scenario. APIs today are enabling interaction between one and other applications as they leverage legacy systems.

Here are the major ways where AI changes testing to maintain the best quality are as follows:

• To optimize test suite and to identify duplicate or similar and unique test cases
• To predict the key software testing parameters based on historical data.
• To identify hotspots and log analytics to execute test cases automatically
• To identify the complex scenarios and trace matrix as well as extract keywords and achieve test coverage.
• To analyze data from distinct social media channels and provide interactive visualization of feedback trends to the specific customer requirements.
• To identify high-risk areas in the application and help prioritization of regression test cases in defecting analytics.

Till now, we have seen the applications of AI in software testing. Let’s have a look at its benefits in software testing

• To enables improvement in quality regarding prediction, prevention and testing using self-learning algorithms.
• Quick testing with fewer efforts using complete E2E test coverage.
• Provide Cognitivity with scientific defect localization approach, enables unattended execution and early feedback.
• Ability to trace missing test coverage and to identify test cases which are not alive for the changed requirement.
• An integrated platform to provide an adaptable landscape to client technology, built on open source stack.

Likewise, Artificial Intelligence is going to play a crucial role in the software testing future. But it’s not going to change this automated software testing overnight. There are several fundamental ideas about quality and testing which will remain for the foreseeable future.

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