The artificial intelligence-augmented software-testing market continues to rapidly evolve. As applications become increasingly complex, AI-augmented testing plays a critical role in helping teams deliver high-quality applications at speed. 

By 2027, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering toolchain, which is a significant increase from 10% in 2022, according to Gartner. AI-augmented software-testing tools assist humans in their testing efforts and reduce the need for human intervention. Overall, these tools streamline, accelerate and improve the test workflow. 

The future of the AI-augmented testing market

Many organizations continue to rely heavily on manual testing and aging technology, but market conditions demand a shift to automation, as well as more intelligent testing that is context-aware. AI-augmented software-testing tools will amplify testing capacity and help to eliminate steps that can be performed more efficiently by intelligent technologies. 

Over the next few years, there will be several trends that drive the adoption of AI-augmented software-testing tools, including increasing complexity of applications, increased adoption of agile and DevOps, shortage of skilled automation engineers and the need for maintainability. All of these factors will continue to drive an increasing need for AI and machine learning (ML) to increase the effectiveness of test creation, reduce the cost of maintenance and drive efficient test loops. Additionally, investment in AI-augmented testing will help software engineering leaders to delight their customers beyond their expectations and ensure production incidents are resolved quickly. 

AI augmentation is the next step in the evolution of software testing and is a crucial element for a strategy to reduce significant business continuity risks when critical applications and services are severely compromised or stop working. 

How generative AI can improve software quality and testing 

AI is transforming software testing by enabling improved test efficacy and faster delivery cycle times. AI-augmented software-testing tools use algorithmic approaches to enhance the productivity of testers and offer a wide range of capabilities across different areas of the test workflow.

There are currently several ways in which generative AI tools can assist software engineering leaders and their teams when it comes to software quality and testing:

  • Authoring test automation code is possible across unit, application programming interface (API) and user interface (UI) for both functional and nonfunctional checks and evaluation. 
  • Generative AI can help with general-impact analysts, such as comparing different versions of use stories, code files and test results for potential risks and causes, as well as to triage flaky tests and defects. 
  • Test data can be generated for populating a database or driving test cases. This could be common sales data, customer relationship management (CRM) and customer contact information, inventory information, or location data with realistic addresses. 
  • Generative AI offers testers a pairing opportunity for training, evaluating and experimenting in new methods and technologies. This will be of less value than that of human peers who actively suggest improved alternatives during pairing exercises. 
  • Converting existing automated test cases from one framework to another is possible, but will require more human engineering effort, and is currently best used as a pairing and learning activity rather than an autonomous one. 

While testers can leverage generative AI technology to assist in their roles, they should also expect a wave of mobile testing applications that are using generative capabilities. 

Software engineering leaders and their teams can exploit the positive impact of AI implications that use LLMs as long as human touch is still involved and integration with the broad landscape of development and testing tools is still improving. However, avoid creating prompts to feed into systems based on large language models (LLMs) if they have the potential to contravene intellectual property laws, or expose a system’s design or its vulnerabilities. 

Software engineering leaders can maximize the value of AI by identifying areas of software testing in their organizations where AI will be most applicable and impactful. Modernize teams’ testing capabilities by establishing a community of practice to share information and lessons and budgeting for training.