Organizations that maintain a “business-as-usual” mindset regarding software development risk falling behind competitors. Speed, dependability, and security need to be an essential part of the SDLC now, and enhanced quality engineering is one of the best ways to ensure these three tenants.
According to the 16th edition of the World Quality Report, generative AI (GenAI) and automation are among the driving forces for this change in quality engineering, also known as quality assurance. In addition to the ability to keep pace with evolving technology, using GenAI and automation provides numerous benefits and risks to an organization in quality engineering. Benefits include increased efficiency and reliability in the development process. It also opens the door to the successful implementation of DevSecOps, which has advantages stretching from DevOps team planning to cybersecurity implementation to post-deployment security for the end user.
Generative AI Is a Friend to Quality Engineers
Chief among GenAI’s benefits is a reduction of costs and defects through the enablement of reliable and scalable testing. According to the World Quality Report, many organizations are already realizing these benefits. For example, 68% of respondents are either actively using GenAI or have completed pilots and are building roadmaps. The stat that is likely most indicative of the adoption of GenAI is that only 4% of those surveyed for the 16th edition said they are not even exploring the use of GenAI. This is a dramatic decline from 31% in the previous report.
While GenAI must be used urgently in quality engineering, the practice is still in its early stages. Many organizations already leveraging GenAI are still experimenting with various use cases and identifying which delivers the most significant benefits. It is still possible to begin integrating AI into the SDLC, but delaying the adoption risks putting a company at risk of falling too far behind.
It is time to tackle the elephant in the room: the temptation to cut costs by reducing quality engineering headcount. For most organizations, using GenAI to replace quality engineers could be a mistake. Instead, using GenAI to significantly increase productivity by enhancing quality engineers’ work would be strategically wise.
Implementing GenAI for routine testing tasks frees engineers to tackle new research and development activities or use their skills for more detailed projects. This, in turn, allows an organization to remain at the forefront of the current technological transformation and future advancements.
Also, if GenAI is implemented into the development phase of the SDLC, a company should include it in the quality assurance phase. One of the primary reasons organizations adopt AI is that it helps developers become more efficient and produce code faster. However, if quality engineering does not adopt AI as well, it will be unable to keep up with the increasing pace of code delivery.
Another word of caution is that while GenAI can contribute to the code quality and design even before it reaches the testing phase, it can also be dangerous. Developers could fall into the habit of trusting AI to the point that they will need to run more tests before handing it over to the quality assurance teams.
GenAI and Test Automation: A Winning Combination
An increasing number of organizations realize they must develop comprehensive automation strategies, align efforts with business outcomes, and invest in modern technologies. Skilled personnel’s essential advantage of automation is that it frees quality engineers from routine, repetitive tasks, allowing them to devote more time to innovation. GenAI comes into play because it accelerates the creation of test-automated (and, if desired, manual and performance-based) scripts. This makes the entire process more efficient.
World Quality Report respondents say the key benefits of using GenAI to enhance test automation include faster automation (72%), easier integrations (68%), and a reduction in testing resources and efforts (62%).
Once the decision is made to transform quality engineering, several things must remain at the top of mind. To implement and maintain a strong, accurate AI-driven testing program, a company should invest in AI to optimize testing by leveraging AI to ensure intelligent products are tested accurately and efficiently. It is also wise to create a blend of GenAI-based digital testers, incorporate human testers to validate product quality aspects that AI and GenAI cannot address, and prepare product quality engineers for evolving technologies.
As with GenAI, automation challenges and risks are associated and avoided with planning. One is enabling less technical people to contribute to the automation rts. A second challenge is increasing the speed of tests created to match the increased developers’ increased velocity. Automated tests’ maintainability is rising, and engineers can sometimes spend more time fixing tests and writing tests. One of the most exciting potential uses of GenAI-fueled quality engineering is leveraging self-healing capabilities. Traditional automated testing often falters when code changes require manual intervention. Autonomous testing is expected to ensure a resilient and adaptable testing environment by leveraging self-healing capabilities to dynamically adjust to code and configuration changes as early as 2025.
DevSecOps: A Unifying Force
If an engineer were to pick an area where Gen AI and test automation play a significant role in the success of the entire SDLC and post-deployment operations, the easy answer could be the emerging practice of DevSecOps. By definition alone, DevSecOps — which stands for the integration of Developer Operations (DevOps), Security Operations (SecOps,) and IT Operations (ITOps) — is a unifying force in the IT world. The over-arching objective of DevSecOps is to improve security seamlessly. It also reshaped the roles of IT professionals and made the software development life cycle faster, more dependable, and cheaper. Furthermore, it is becoming more apparent that AI can be a powerful force in DevSecOps, especially when test automation is part of the strategy.
GenAI and test automation allow security to seamlessly become a part of the planning and development of software instead of at the end of the cycle or after deployment. With GenAI and test automation, DevOps and SecOps engineers can view potential issues with DevSecOps platforms that scan code, run comprehensive tests, and monitor systems in real-time. GenAI can go beyond observation by playing a role in the management of projects by helping to surface the risks, suggesting mitigation, and assisting the relevant stakeholders in focusing on the most impactful and risky items first.
The result is a consolidated, more efficient development process that produces more effective products. In addition to the savings during the SDLC, DevSecOpsdramaticallyy reduces the risks of successful cyberattacks, which can cost millions, if not billions, of dollars, unplanned extended downtime, damaged reputations, and the wrath of unhappy customers.
Future use of GenAI, Test Automation, and DevSecOps?
It is anticipated that in 2025, more integrated DevSecOps pipelines will become increasingly prominent while breaking down silos between development, quality engineering, ng, and security teams. This unification opens the door for faster software releases and reduces vulnerabilities by embedding testing and security checks early and often throughout the pipeline, aided by AI-driven insights.
One key point about an AI-powered DevSecOps program is that it instills organizations with a new proactive cybersecurity mindset rather than the traditional reactive posture. Why is this important? While AI is a tool for good, it can also be used to commit cyberattacks. In 2024, the U.S. Federal Bureau of Investigation issued a warn024 about cybercriminals using AI to increase cyberattack speed, scale, and automation. The best way to fight AI is with AI.
As AI data quality engineering and accuracy improve, the industry will begin to overcome skepticism about AI’s trustworthiness, reliability, and security. Organizations that embrace this shift will help address gaps in comprehensive test automation, strengthen data integrity, and reduce reliance on legacy systems. Thus, they will be stronger and better prepared for the next inevitable technology transformation.
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