Software quality engineering company Tricentis has highlighted analysis which suggests that testing is becoming a more prevalent and pressing item on the agenda of progressive teams’ development lifecycle agendas.
Its report, AI-augmented DevOps: Trends Shaping the Future aims to understand to what extent the anticipated benefits of AI in DevOps have been realized today and how a lack of trust, skills, or other challenges could affect its adoption.
When asked to evaluate the most impactful areas for AI investments across the delivery cycle – such as planning, coding, deploying and releasing – DevOps practitioners ranked testing as the most valuable (60%).
This result was foreshadowed in the Tricentis’ 2022 study, which found that testing is where organizations expected the greatest value from AI-augmented DevOps, with nearly 70% of respondents having rated the potential of AI-augmented testing as extremely or very valuable.
Mature DevOps
The research finds that DevOps teams are realizing the benefits of AI, with mature DevOps teams who have adopted AI significantly more likely (30%) to rate their teams as either extremely or very effective. The biggest challenges DevOps teams are using AI to address are developer team efficiency (60%), reducing the skills gap (54%), cost reduction (47%), and software quality (42%). In fact, almost one third (32%) of respondents estimate AI-augmented DevOps tools will save teams over 40 hours per month—equivalent to an entire workweek.
“AI is an exciting technology and growing at a pace unlike anything we’ve seen in our industry,” said Mav Turner, chief product and strategy officer, Tricentis. “As AI technology is further developed, however, training software development and quality engineering teams with the necessary skills to effectively work with AI will be absolutely critical.”
The 2024 results show that teams use AI to augment a wide range of testing tasks, including test planning/deciding what to test (47.5%), test case generation (44%) and analyzing test results (32%). Additionally, nearly half (42%) of respondents expect AI to perform a risk analysis of code changes, helping QA teams focus on code areas with the greatest risk of errors to quality.
Testing is pivotal
“DevOps teams looking to get started with AI should [start by looking at] their testing processes,” said Turner. “AI in testing helps to detect, auto-heal and predict defects during development, as well as identify which tests need to be run based on high risk. When coupled with low-code/no-code technology, this means that, regardless of a team’s technical expertise, AI can significantly contribute to overall software quality. As DevOps teams continue to mature, testing will be pivotal to realizing their investment in AI-augmented DevOps tools and practices.”
The report surveys 500+ DevOps practitioners, managers, and executives from small, mid-size, and enterprise organizations across the globe and in several industries, including financial services, healthcare, and manufacturing.
