Artificial Intelligence in Quality Assurance: From Manual to Autonomous Testing Using AI

Introduction

If we observe the software development journey since the existence of the agile approach, we will discover that software delivery methods have dramatically transformed. Prior to Agile, apps and software were released once a month or bi-monthly. However, agile implementations have resulted in app releases every week, bi-weekly, or even daily. To keep up with these frequent releases, continuous testing and automation suites for sanity and regression testing were developed. This new AI-powered testing approach advocated faster deliveries and accelerated testing cycles, even allowing multiple deliveries per day. But before deep diving into how AI impacts quality assurance and testing, we should first understand what AI in Quality Assurance means and why it is crucial for organizations like you.

Why and What is AI in Quality Assurance?

Artificial Intelligence is a technology-friendly invention revolutionizing industries with immense benefits and potential. It influences the quality assurance process, involving creating test data sets and data to check the system or software quality through automation or by streamlining the software development lifecycle.

Humans inherently have subjective prejudice, notably in manual quality testing, which increases the risks of human errors with more cost and time. This challenge is even more prominent when applications are being developed and deployed across multiple platforms.

AI can help you overcome these challenges and accelerate the testing process without human intervention. It can predict client behavior, detect fraudulence that is not captured with traditional functional tests, and assist in targeted marketing by replicating manual activities. It eliminates test coverage overlaps, optimizes test automation, and improves agility and predictability through self-learning. The QA teams can leverage AI testing tools to improve normal testing efforts with expedited time and greater accuracy.

According to Gartner’s Competitive Landscape Application Testing Services report published on 25 September 2019 by Susanne Matson, “By 2021, intelligent automation will generate an additional 20% savings over what is achievable today in application testing services for end-users.”

Role of Artificial Intelligence in Quality Assurance

AI makes Quality Assurance processes leaner and more efficient. Several AI methods and techniques are being applied in QA, including time spent on testing, ensuring complete test coverage, increasing focus on defect hubs, and accelerating the release process to enable a faster time to market. The AI testing tools can help perform tests with AI-powered visual verifications, which, in turn, give out a range of various outcomes. Organizations are already using AI for image-based testing, AI spidering, monitoring API testing, and automated testing tasks. As artificial intelligence becomes more ubiquitous, testers will find it easier and more efficient to create, execute, and analyze software test cases without continually updating them manually. Additionally, they will be able to identify controls and discover the link between defects and software components more readily than ever before.

Steps for Developing AI in Quality Assurance

Here we are mentioning the five essential steps of developing top-notch AI in QA, which include:

• Pilot
• Data Annotation
• Test and Validate
• Scaled Deployment to Production
• Retraining

Phase 1 and 2: Pilot and Data Annotation

In this phase, the QA team typically identifies what testing they will do and what they hope to accomplish. After setting the scope and objectives of testing, the QA teams need to start collecting the required data. Testing methods need to be selected based on the scope of the project’s scope uses and the AI models and algorithms used during testing. This will ensure that the data used to train the models is of the highest standard.

Phase 3 and 4: Testing and Validation as well as Scaling

In this phase, the QA team develops test algorithms multiple times and selects a portion of the training data to validate the test. Then they put this training data in a scenario and evaluate how the algorithm performs with it, observe the data behavior, and verify whether the AI model sends predictive results accurately and consistently or not.

Phase 5: Retrain

Every AI model requires regular Retraining. After testing and validating your AI model performance for the QA test, the next step is retraining your ML model or continuously improving the model in line with current features. The objective of this is to ensure that the quality of your AI model in QA is up to date, gives appropriate quality results, and provides the chance to enhance accuracy.

Benefits of Artificial Intelligence-led Quality Assurance

Now let us see what the essential and salient benefits of AI-driven Quality Assurance are:

1. Reduce test fatigue

AI in Quality assurance can save up to 60% of your time and effort by eliminating duplicate test cases & repeat runs of smoke tests & regression tests, which in turn will help you be more productive. Based on the risk detected using machine learning algorithms for the functionality under test, the platform gives an insight into the test cases that should be conducted.

 2. Prolonged traceability

Pointing to the complexities of business functions and objectives, as opposed to its vulnerabilities, aids in the (Go/No- go) decision-making process. This is primarily connected with the Release Management function, which increases customer satisfaction by ensuring the product is launched to market without any vulnerabilities. The traceability is also an in-built automated feature that gives comprehensive coverage and confidence.

3. Assurance of business processes

By verifying individual features and functionalities, AI ensures that applications and services match the business and consumer needs. Moreover, it examines essential business processes throughout the organization and visually maps application or service risks on a risk matrix. This dashboard provides a comprehensive picture of risks and vulnerabilities in a company’s business operations.

4. Predicting weak spots

AI also helps predict failure locations and gives engineers insights into functions requiring more testing. Moreover, AI delivers insights based on past events for the application under test, leveraging production data and past project experiences.

5. Release of a well-researched build

Artificial intelligence allows AI development organizations to compare similar apps and software to see what factors led to their market success. Moreover, new test cases may be created after identifying the market needs to ensure that the app or program does not break when reaching specific goals.

6. Effortless test designing

A sizeable portion of Quality Assurance professionals’ work is confined to test design scenarios. The same procedure must be followed every time the latest version is launched.

AI QA automation solutions may assist testers in developing script less or low code automation of test scripts analyzing the app by scrolling through each page and generating and running test case scenarios for them, thereby reducing preparation time.

Future of Quality Assurance – Autonomous Software Testing 

Autonomous testing involving automated creation, test maintenance, and execution through AI/ML is the next frontier in quality assurance. It is being implemented at a rapid rate to transform software testing to the next level.

Autonomous Testing analyses the collected data and produces insights and predictions to build test suites and all artifacts usually created manually throughout the testing life cycle. However, the autonomous testing solutions are still in their genesis, and most organizations are uncertain about how to start implementing test automation frameworks while leveraging AI and ML in their testing practices.

Conclusion – Hexaware’s Journey towards Autonomous Software Testing 

Implementing autonomous testing for its customers, Hexaware has discovered over 100+ use cases for functional and non-functional testing. Hexaware advanced autonomous test platform, Hexaware Autonomous Test Orchestration Platform (ATOP), utilizes AI, machine learning, and deep learning algorithms mixed with natural language processing techniques to enable the transformation to move from Automation to Autonomous Testing. It collects the data generated during various phases of the application life cycle and analyses it with the creation of inferences with decision-making capabilities leveraging AI/ML technologies. The ATOP has out-of-the-box integrations with important application lifecycle management tools, application performance management tools, log tracking tools, defect management tools, and app stores. To learn more about Hexaware Autonomous Software Testing offerings, visit End-to-end Autonomous Software Testing.