LambdaTest’s “Future of Quality Assurance 2023” study, which surveyed 1,615 software testing professionals across 70 countries, revealed intriguing insights into current software testing practices. Notably, 78% of respondents have already embraced AI in their testing processes.
The year 2023 witnessed several high-profile software failures in the USA, impacting sectors like financial markets and air traffic, leading to significant disruptions. Research by Dr. Junade Ali CEng FIET indicated that 71% of software engineers expressed concerns about software reliability in their workplaces, marking a substantial increase from 68% since 2021.
Expanding on these observations, LambdaTest’s research indicated a notable shift in the approach toward software quality. Approximately 72% of organizations now involve testers in “sprint” planning sessions, showcasing a significant move toward considering software quality earlier in the software development lifecycle. This trend reflects a growing emphasis on addressing the need for more excellent software reliability.
The survey findings indicate a significant integration of AI technologies in software testing practices, revealing widespread adoption for tasks such as test data creation, automated code writing, test result analysis, and test case formulation. 89% of organizations embrace automation through CI/CD tools for deploying and running tests, underscoring the industry’s drive for efficiency and speed.
“At LambdaTest, we understand that while the adoption of AI is a significant step forward, the journey doesn’t end here. For example, our study highlights the need to address bottlenecks affecting productivity like brittle tests alongside the set-up and maintenance of test environments. This presents us with an opportunity as well as a challenge – to develop and implement tools that will efficiently address these bottlenecks to keep driving software quality forward.” – ASAD KHAN, CEO & Co-Founder of LambdaTesT
Dr. Junade Ali CEng FIET, CEO of Engprax, a software auditing firm, lauded the recent study for its expansive insights into software reliability and testing trends in 2023. The study underscores organizations’ efforts to bridge the disparity between market expectations for software reliability and the current status quo. Notably, the rapid integration of Artificial Intelligence within software testing processes has been evident, yet efficiency hurdles persist in enhancing the cost-effectiveness and speed of testing procedures. Dr. Ali emphasized the significant pressures software testers and QA staff face in the software development lifecycle. While involving them earlier in the process marks progress, new tools present a promising opportunity to narrow this gap further.
However, despite these advancements, challenges persist in the software testing landscape. Setting up and maintaining test environments consumes 10% of team time, and an additional 8% is spent on rectifying unreliable or “flaky” tests. A lack of structured prioritization mechanisms affects 74% of teams, potentially overlooking crucial factors like risk assessment and customer feedback in automated testing processes. Moreover, many teams (29%) lack Test Intelligence infrastructure for insights into automated test performance, and 12% lack proper reporting systems, highlighting deficiencies in measuring software reliability.
FAQs
1. How have organizations shifted their approach toward software quality?
There’s been a notable shift, with around 72% of organizations involving testers in “sprint” planning sessions. This indicates a move to consider software quality earlier in the software development lifecycle, emphasizing the need for better reliability.
2. What challenges are highlighted in the software testing landscape despite AI adoption?
Challenges include time-consuming tasks like setting up and maintaining test environments (consuming 10% of team time), dealing with unreliable or “flaky” tests (using an additional 8% of the time), and the lack of structured prioritization mechanisms (affecting 74% of teams).
3. What efficiency hurdles are faced despite AI integration in testing processes?
While AI adoption has brought advancements, efficiency hurdles remain, particularly in addressing brittle tests and the setup/maintenance of test environments. These challenges present opportunities to develop tools to enhance software quality assurance efficiency.
[To share your insights with us, please write to sghosh@martechseries.com]
