DEVOPSdigest

Artificial intelligence (AI) has dominated business conversations and news cycles across the world since ChatGPT was first launched last November. Companies have touted AI's ability to make employees more productive and efficient, personalize services and experiences, and improve quality while decreasing human error. And today, many organizations also realize the competitive advantage of utilizing AI in workflows, especially in web and mobile application performance testing.

For leaders seeking to maximize the return on their AI investments, it's imperative to understand AI's benefits and how to best implement the technology into the team's testing processes. There are many different use cases for AI in software testing, as well as certain steps teams can follow to ensure a smooth implementation process. This blog will take a closer look at how to do this, so teams can successfully navigate and implement AI into testing environments.

Reaping AI's Benefits in Software Testing

Teams can experience many benefits incorporating AI into performance testing workflows. Two of the biggest benefits teams realize right away are improved quality and increased efficiency. Not only does AI ensure the successful production of the application being tested, but it also decreases testing time. Since an AI tool is doing a task previously done by a human, it also reduces manual errors.

Additionally, AI tools are beneficial to teammates with a range of skills and experience, from new hires to seasoned veterans. With the help of AI, any employee across the board can run and understand performance tests. In turn, testers receive a boost of confidence in their abilities, allowing them to improve their skillset and gain more knowledge with AI tools' support.

When it comes to software testing specifically, the opportunities for testers to reap AI's benefits are seemingly limitless and make their jobs easier, cutting out much of the tedious, manual labor that goes into conducting a thorough test. One of the best ways to use AI is to create and synchronize test data. AI can also take over repetitive tasks, like auto-generating tests, thereby making testers' jobs easier. Another use case is deploying AI to understand test results and relay those results to the team without needing a human to spend time deciphering them. Overall, each use case of AI makes the team more efficient, saving time, money, and resources.

Embracing and Implementing AI

Engineering and application development teams must embrace AI or they risk falling behind competitors. It can be daunting to implement AI in the software testing process, especially if the team hasn't used this technology in any part of the development cycle. The first step teams must take on their AI implementation journey is to educate themselves on the technology, the different AI tools on the market, and what they can offer the team.

The second step is crucial as well: plan. Development teams won't reap AI's benefits if they pick a tool at random and hope for the best. It is imperative that team leaders make a strategic plan on how they will roll out AI, how it will benefit the team, and how it fits into existing workflows. It's possible that teams need to develop new processes after implementing AI, which is another factor leaders should plan for.

The next steps aren't hard-and-fast rules for implementing AI in performance testing, but teams will realize more benefits if followed. Automation, verification, and security are some of the other steps teams can execute. AI tools can automate different parts of the workflow, such as running tests for each individual new feature, resulting in increased efficiency and decreased manual labor. AI models could create false information, known as hallucinations. Before utilizing the AI tool, it's vital to verify the results and ensure all information is accurate. Security is top of mind for both the security and developer teams. As teams deploy AI, they must consider different compliance and safety policies in place to protect proprietary data and client privacy.

Putting It All Together

Armed with these tools, teams can experience the many benefits of AI in their performance testing workflows. The improved efficiency and reduced testing errors will cause teams to wonder why they didn't implement AI into their testing strategies sooner.