How to measure the impact of Gen AI on software coding and unit testing?

Discover the importance of a robust measurement protocol to ensure measurable results

Generative AI is revolutionizing how businesses approach software engineering, offering tools to accelerate workflows, automate processes, and reduce technical debt. By enabling organizations to balance speed with quality, Gen AI is driving a significant shift in how software is designed, developed, and tested.

While the benefits of generative AI are clear, most organizations lack the tools to measure its full impact. The complexity of software development – encompassing factors like productivity, quality, and developer experience – makes effective measurement difficult. Stakeholders often prioritize different dimensions, from usability to security, further complicating evaluation. Without a clear measurement protocol, organizations risk falling short of realizing the full potential of Gen AI.

Adopting generative AI is only part of the journey – measuring its impact is critical for sustained success. A robust measurement framework ensures that Gen AI delivers on its promises while aligning with broader business objectives.

In this paper, we offer actionable guidance for organizations seeking to leverage generative AI in software engineering. Learn how to define metrics, overcome challenges, and maximize impact to deliver better software faster, improve team productivity, and enhance the overall developer experience.