The Growing Significance Of AI For Software Testing In 2021

The impact of the various applications of AI in automation testing is well known. Moreover, AI is widely accepted as an essential accompaniment for the seamless delivery of any software product these days.

When it comes to software testing, automation offers clear benefits for:

• Filtering issues 
• Consolidating human efforts 
• Scaling with increasing demand
• Providing actionable test case recommendations 
• and learning from human decisions

AI helps organizations make up for the delivery compromises that come with faster product delivery, cost-cutting, and reserving time for R&D. All these factors have become irreplaceable in the software development cycle because of the mounting competition among businesses.

In this article, we will discuss the enhancement of software testing capabilities with AI-powered automation.

Why Has Software Testing Become Highly AI Dependent In 2021?

A long list of AI capabilities contributes to enhancing productivity in the test workflow for software quality analysts. Based on a particular organization’s business requirements, these capabilities can be assorted and tailored as well. 

As per an EMA Research Report, test automation platforms and scripts have taken up the majority of software test cycles since the beginning of 2020. This is because the number of tasks branching out from the primary software development workflow has multiplied.

In addition to the primary workflow, a number of associated tasks also need to be tested simultaneously. Distributed microservices and devices running across a wide array of browsers and networks need to be tested for co-compatibility.  

While testing all these components involved in the development of software, quality analysts face a number of issues. Let us explore the various ways in which AI attempts to overcome them.

How AI Helps To Overcome Testing Issues?

Quality analysts are faced with several challenges no matter what the type of testing; manual or automated, regression or unit. The following are these challenges in detail and the ways in which AI overcomes them:

False Positives

Test workflows often fail in differentiating between actual bugs in the software and small changes in the code. Test engineers tend to run short on time to write these workflows. Most of the deviations are tagged for human review. Resultantly they cannot reliably differentiate between changes in code, slowing down the release process.

AI or Machine Learning (ML) algorithms can continuously monitor usage patterns for test engineers. They can then map various changes to understand normal end-user behavior. Coinciding support tickets are observed for changes in volume, content, and severity of changes in application error logs. Additionally, the AI will alert the analysts or engineers about potential issues in the application code.

Regression Testing Maintenance

Every release comes with a bunch of regression tests to prevent negative results from the interaction between old and new code. Based on the code changes, creating rule-based regression tests is a labor-intensive task for test engineers. This is because they need to continuously adjust the code.

Automated maintenance of test results with AI analysis and replication of human supervision of regression tests is what AI brings to the table. It provides complete and accurate regression of the UI. As a result, development teams spend less time on the management of test code. A full set of regression tests can be run before the new code is released.

Inefficient Feedback Workflow

The successful combination of the human intellect and test automation can only happen with well-planned test workflows. Optimal test efficiency can be ensured if bug reports, code reviews, and related compliance information are at the test engineer’s disposal. However, they tend to lack the ability to concisely learn the end-user requirements.

AI-driven workflows bridge this gap by continuously monitoring bug reports and other relevant factors. They alert test engineers and provide situational context to enhance productivity. Remediation scripts and test parameters are also provided by the AI as and when the need arises.

Code Complexity

Any change in the code of distributed microservices-run applications can affect the data of any other microservice. The functioning of the microservices architecture is affected and as a result, the end-user experience is not up to the mark. In modern distributed applications, this code complexity is very hard to navigate manually.

Enterprises need to prioritize the use of their limited resources if they are to successfully circumvent application complexity. AI can help by covering for random gaps in test coverage in distributed applications. It can identify where the impact of the code changes has been felt. It can detect the application stack and correlate external dependencies in the user experience based on continuous visual inspection of the UI.

Testing On Target Devices

There are over 1000 devices combining the existing Apple and Android devices with new entrants in the 2021 market. A majority of software providers only test their applications for a tiny bracket of devices, mostly flagship devices. This is done in order to reduce the time spent until product release. As a result, users of devices wherein the application has not been properly tested tend to face unresolvable issues.

AI implementation in multi-device testing can help test engineers focus their efforts on other more critical tasks. This leads to the early detection of problems in specific types of devices to alert engineers of high-priority issues where manual intervention is required.

Test Tool Chains

Completion of tests in different areas such as UI, API, scalability, performance, and stress can be viewed as release gates. As each factor is tested it brings the product closer to the end of the software release life cycle. The test tool chain must integrate with the CI/CD pipeline to continually pass these release gates, but that is rarely the case with manual testing.

AI can switch between different test suites and reduce the overt dependability of specialized tools. This is done by testing functionality from the front end. At the UI layer, the desired inputs and outputs are also defined by AI to reduce the tool chain complexity of testing.

AI-Based Test Automation Enables Real ROI Delivery

Today’s critical software testing bottlenecks that arise from scalability can be overcome with AI-based test automation technologies. Organizations need to utilize the capabilities of AI for automatic issue qualification and assignment to human test engineers. This helps in curbing software release overhead to truly maximize ROI. 

To help your software product be free from redundancies, compatibility issues, and other flaws, test automation is the way to go. To know how Daffodil can help with your transition to AI-based test automation, you can book a free consultation today.