The key to unlocking the next era of software delivery is automated testing

Over the past few years, AI has invaded our personal and professional lives like a bullet train. While few would deny the convenience it provides, it’s important to remember that we are passengers. In that short time, AI has already significantly improved car safety, streamlined planning, personalized online shopping, and even supercharged search engines.

Like any high-speed train, the rapid pace of AI requires adaptation. AI is accelerating code creation in software development, forcing companies to adapt by streamlining testing processes to keep up with demand.

According to a recent report from GitClear, 92% of US developers working at large companies use an AI coding tool, and 55% report faster coding. AI coding tools are accelerating productivity gains, but they have also created obstacles throughout the SDLC that lead to bottlenecks in testing processes.

Manual testing has always been fraught with difficulties. The proliferation of AI has highlighted the fact that increased code can potentially undermine an already fragile process in the SDLC. Manual testing can create slow and inefficient processes that are laborious and expensive. The real challenge with manual testing is the risk of human error, and it is technically unscalable.

To streamline the code review process, automated testing tools help overcome these challenges with increased efficiency and visibility, among other key benefits. In fact, according to Gartner’s 2022 report, “Market guide for AI-augmented software testing tools,” 48% of organizations are using AI and ML in their testing, and that number is expected to grow to 64% by 2025.

Automation testing, the use of software tools to automatically execute tests and analyze the results, promises to significantly alleviate the long-standing pains of testing, offering tools such as CI/CD integrations, AI-powered test creation, self-healing, and predictive risk assessment that increase efficiency and transparency while saving developers time and reducing risk.

Turning the Key: Overcoming Software Testing Barriers with AI-Driven Solutions

More code means more testing. To keep up with consumer market demands and stand out from competitors while efficiently producing high-quality software, you need to balance code productivity with AI model quality. The average commercial project has an average of 40 first-party code vulnerabilities. According to Snyk’s 2023 data, AI co-pilots can replicate and exacerbate vulnerabilities.

Automated testing tools provide the capabilities needed to handle the growing number of tasks that development teams may face, offering the following benefits:

• Reduced testing time – According to the report “The state of AI in quality engineering” published by Capgemini in 2023, automated testing techniques reduce testing time by 50%, giving developers and testers more time to focus on other aspects of the SDLC.
• Reduced number of errors – The accuracy provided by automated testing tools reduces the number of code defects that make it to production development. A lower defect escape rate reduces developer friction and streamlines the code push process. In 2023, Capgemini also reported a 40% reduction in defect escape rate with AI-powered testing tools.
• Increased Test Coverage – AI-powered testing tools provide throughput and self-healing capabilities to increase the amount of code being tested. They also further simplify test creation by increasing the number of test scripts being written. Capgemini reports a 30% increase in test coverage due to these improvements.

Unlocking Quality: AI and ML Are Creating the Key to Continuous, Automated Testing

The evolution of AI and ML in testing is changing the game for every large enterprise building and delivering web and mobile applications. As this continues, enterprises can expect to see increased innovation in the continuous automated testing market, with applications such as:

• Synthetic Test Data Generators – Leverage AI to create realistic, anonymized test data sets that create broad and diverse test scenarios, uncovering edge cases and pushing the boundaries of what enterprises can test.
• Intelligent Test Assistants – Use machine learning to analyze test data, identify patterns, and even suggest test case improvements. It’s a collaboration that significantly increases tester productivity and efficiency.

As organizations continue to adopt these techniques, they will unlock a future of higher-quality software and improved user experiences that will allow them to dominate the competition. Enterprises are witnessing growth and expansion in software development similar to the productivity leaps during the Industrial Revolution. AI-powered testing tools are not becoming common by accident; they are reaching the mainstream because they are the only option to keep up with innovation.

AI is enabling testing to reach a new level of sophistication that predicts and uncovers hidden problems in code, something that has become necessary as the AI ​​development express train continues to accelerate code volume. This promises a future of not only faster testing, but also a drastic leap in overall software quality and reliability.