AI In Testing Software: What Does The Future Hold?

AI In Testing Software: What Does The Future Hold?

December 28, 2021
Prathitha IyengarPrathitha Iyengar
Prathitha Iyengar

Artificial intelligence (AI) is currently the backbone in influencing and transforming many sectors, impacting our daily lives. Industries like healthcare, education, finance, technology, and retail are adopting AI to work efficiently, reduce costs, and automate tasks. Not just in these areas but also in our homes, AI is taking control of televisions, cell phones, digital assistants, home automation, and security cameras.

Capture audio and video from any device placed inside the appliance. Know more!

As AI is in a constant race to exercise more control over our lives, it is essential to create and validate all systems safely and securely. This point is where AI for software testing comes into place. 

This article will discuss how you can use AI in software testing. Subsequently, we will understand how the HeadSpin platform, with its exclusive features, helps in an exceptional software testing process.

What is Artificial Intelligence (AI)?

Artificial Intelligence or AI is the ability of a computer-controlled system (computer) or a robot to complete tasks usually done by human beings; they mimic human intelligence and insight while executing these tasks. 

In simple words, we can say that we teach computers or robots or machines to think and learn like a human. This field of study tries to make computers intelligent.

Check out: How Intelligent Automation is Disrupting the BFSI Industry

Examples of AI include automatic vehicles, Alexa, Siri, and other smart assistants, computer bots, social media monitoring, disease mapping, email spam filters, etc.

What is Software Testing?

Software testing is a process that involves evaluation and verification of any software or application to check whether it is working correctly and whether it does what it is supposed to do. 

This process also matches the standard requirements with the actual ones. It ensures that the software and application are free from defects and bugs.

Recommended Post: Machine Learning in Test Automation - 6 Things to Be Considered

Importance of Software Testing

Suppose you find any error, defect, or bug.  In that case, you can identify and solve it with the help of software testing before delivering the software. Thus, you can reduce included development costs and improve end-user performance.

The next section will explain how to use AI while testing software applications.

AI-based Testing

The AI technology helps test software effectively and appropriately before deployment, ensuring high performance, security, and reliability. These factors result in customer satisfaction, time-saving, and cost-effectiveness in the long run.

The core objective of AI in software testing is to make the testing process smooth, smart, and highly effective. AI testing tools are used for logical reasoning, problem-solving, executing tests, and checking and testing algorithms to improve the overall testing process.

Also check: 5 Popular Test Automation Tools for React Native Apps

Here are some benefits of testing software with AI:

  1. Improved accuracy
  2. Visual validation
  3. Better test coverage
  4. Faster time to market
  5. Lesser defects, errors, and bugs
  6. Saving of time, money, and efforts

AI for Software Testing: Current State

‘Test bots’ is what we refer to when we talk about AI in its current state. They perform activities such as failure detection, modeling, application discovery, and test generation.

Some of the past decades’ AI-driven testing approaches are:

  1. Visual Testing: To support image-based learning and test the look and feel of an application.
  2. Declarative Testing: Telling systems how to determine what and carry out the test.
  3. Differential Testing: Performing different applications, comparing different versions, classifying, and learning from the feedback.
  4. Self-healing Automation: Autocorrection

AI and Software Testing: Future State

There are AI trends that started in the past decade. Mobile application testing, user interface testing, functional testing of the web, and auto-correction are examples of this type where AI is in use. 

These AI functions are so well-performing that there is an expectation that they will continue in use and will help in enhancing the framework and tools for testing specific problems.

There will come a time when AI will take over the automation tasks in the future. There will be technological progress, and we will train machines for higher-order assignments. AI will take over the tasks and tackle issues requiring deeper thinking contexts.

How HeadSpin is Leveraging AI to Revolutionize Software Testing 

HeadSpin leverages advanced AI and ML capabilities in its mobile testing platform to revolutionize software testing for enterprises. By incorporating AI-driven automated testing solutions, HeadSpin enables efficient identification and analysis of issues, ensuring high-quality experiences across applications, devices, networks, and locations. Here are the key ways HeadSpin utilizes AI to facilitate effective testing:

  • AI-based Performance Monitoring: HeadSpin's AI-enabled engine assesses user journeys, captures essential performance KPIs, identifies root causes of poor experience issues, and delivers actionable insights. It offers a secure cloud environment— a global device infrastructure spanning 90+ global locations to facilitate end-to-end automated testing. This approach enhances customer experience, reduces time-to-market, and optimizes the cost of digital applications.
  • Regression Intelligence for Proactive Issue Detection: HeadSpin's Regression Intelligence enables thorough regression testing powered by AI, helping companies proactively detect and resolve performance, functionality, and localization issues. It provides insights into performance and UX trends, as well as the behavior of third-party APIs and SDKs.
  • Audio/Visual AI for End-to-End Testing: The HeadSpin Digital Experience AI Platform ensures optimal performance and QoE analytics of mobile, web, audio, and video experiences throughout the development lifecycle. It allows simultaneous execution and compilation of testing scenarios and user sessions, providing real-time results. HeadSpin's AV platform leverages AI capabilities to analyze various metrics such as blurriness, brightness, loading/buffering time, and audio/video quality. The Platforms' AI-based reference-free video MOS model provides a true subjective quality score estimation of end-users' perception.

What Makes HeadSpin's AI Capabilities More Advanced than Contemporary AI-based Testing Practices?

HeadSpin's approach to AI involves collaborative problem-solving, continuous improvement through user feedback, and a strong commitment to privacy. These factors combine to provide effective performance monitoring and optimization for distributed applications.

HeadSpin's AI approach stands out in several ways:

  • Human collaboration: HeadSpin's AI works in conjunction with human experts to address issues in distributed applications. Its AI models have been created by analyzing millions of data points collated by industry experts. Leveraging the combined power of AI and human expertise, HeadSpin makes for more effective debugging and problem-solving.
  • Learning systems with user feedback: HeadSpin transforms heuristics expert systems into learning systems that continuously improve based on feedback from end users. This iterative process allows the fine-tuning of AI models, catering to specific customer use cases.
  • Privacy-focused approach: HeadSpin's AI models are designed to prioritize privacy. By detecting regions of poor user experience and performance without the need to track or monitor end-user data, we ensure that sensitive releases and user information remain confidential. Customers can gain valuable insights while maintaining data privacy.


Artificial Intelligence (AI) has been there for many years. Companies such as Apple, Google, Facebook, and Amazon have been actively taking an interest in this technology for various applications. 

With the help of AI, companies can fuel their growth and use of AI. The trend will continue with the testers’ community, who have been consistently using AI for software testing.

HeadSpin has been making software testing easier using AI technology with its seamless integration with CI/CD workflows that enables functional and performance testing before and after release. It supports all the testing frameworks, including native Appium. 

Moreover, HeadSpin’s AI platform surfaces issues automatically and recognizes underlying root causes across all stack layers, from quantifying audio/video QoE to code visibility. The platform also delivers regression and aggregation insights throughout the digital experience, from planning and development to troubleshooting and operations.

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1. What are self-healing tools?

In automation testing, the issues of flakiness, reliability, and maintenance persist throughout, which is one of the primary reasons why AI and ML are introduced in test automation. To address these issues, self-healing tools have been developed, which are generally based on a record and playback mechanism, where the primary ML engine resides in the self-healing of the recorded scripts. 

2. How has Robotic Process Automation impacted test automation?

Evidently, RPA has helped substitute manual effort in load, regression, and performance testing, where testers can easily focus on usability and exploratory testing. Some of the key benefits that RPA offers are:

  • Better quality and well-maintained products that can lead to better customer service
  • Increases efficiency in digitizing process data
  • Reduces the cost by decreasing the requirement for manual effort
  • Ensure that business processes comply with the expected standards and regulations
Read: Using Appium for Robotic Process Automation (RPA)

3. What is spidering AI?

One of the latest AI-based automation techniques leveraged by the developers is the spidering method that automatically writes tests for your application. All that you need is to point some of the newer AI/ML tools at the web application for initiating crawling.

Also read: A Complete Guide to Web App Testing

4. What are some of the different types of testing frameworks?

Following are a few important framework types:

  • Keyword-driven framework
  • Data-driven framework
  • Hybrid Framework
  • Linear Scripting

AI In Testing Software: What Does The Future Hold?

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