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.

How is Generative AI transforming the way businesses handle software engineering?

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 it boosts productivity without compromising quality, full benefits require expert-led measurement.

How will Gen AI impact software programming?

Generative AI is transforming software development by moving the focus from manual coding to prompt engineering and code validation. Its key benefits include:

• Enabling innovation: Supports teams in developing new features and upskilling employees.
• Improving software quality: Assists with code autocompletion, unit test generation, documentation, and code migration.
• Increasing productivity: Boosts efficiency without compromising quality.
• Ensuring robust testing: Generates synthetic data and enhances unit test coverage for real-world scenarios.

While adoption is still early, many organizations are experimenting with Gen AI, and some have started integrating it into software workflows. Over the coming years, Gen AI is expected to augment software design, development, and testing, providing advanced tools and platforms while maintaining high-quality outcomes.

Why measuring Gen AI impact matters and the challenges involved.

“What gets measured, gets managed.” Measuring Gen AI’s effectiveness is critical to understanding its true impact.

Challenges in measuring Gen AI impact:

• Complex productivity assessment: Software work is dynamic and multifaceted, making productivity hard to quantify.
• Holistic evaluation: Requires qualitative and quantitative factors tailored to context.
• Software quality metrics: Must consider functionality, performance, reliability, usability, maintainability, security, and scalability.
• Stakeholder priorities: Different stakeholders value different outcomes.
• Developer feedback: Daily users’ insights are essential for evaluating real-world effectiveness and adoption challenges.

Establishing a measurement protocol

While generative AI offers clear benefits, many organizations struggle to measure its full impact due to the complexity of software development and varying stakeholder priorities. Implementing a robust measurement framework is essential to track productivity, quality, and developer experience, ensuring Gen AI delivers value and aligns with business goals.

Key components of the measurement protocol:

• Team organization: Single or multiple teams with varying seniority for normalization.
• Measurement approach: Define timeline, preparation, baseline, and execution process.
• Metrics: Coding velocity, unit test coverage, code efficiency, security, code smells, duplication.
• Tools: SonarQube, CAST, Jira, developer surveys.
• Reporting: Templates for detailed and executive-level results.
• Normalization and feedback: Adjust for team size, complexity, and gather qualitative insights.

Impact of Gen AI on software coding and unit testing

At Capgemini, internal and client-based experiments evaluated the impact of Gen AI on software coding and unit testing, delivering promising results across teams of all levels.

Key findings:

• Developers love it: Feedback has been overwhelmingly positive. Gen AI helps junior developers ramp up faster, while senior developers focus on mentoring, tackling complex issues, and collaborating with business teams.
• Increased velocity: Coding and unit testing productivity improved by 10–30%, depending on task complexity and context.
• Benefits across teams: Junior teams benefit the most, though senior teams also show measurable gains.
• Maintained code quality: No quality degradation was observed, as confirmed through static analysis and manual code reviews.
• Improved documentation: Automated documentation enhanced metrics and freed developers for higher-value tasks.
• Enhanced unit testing: Gen AI improved test coverage, ensuring reliability and functional correctness.

In this report, 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. Download the full report “How to measure the impact of Gen AI on software coding” to explore detailed insights, measurement protocols, and best practices for maximizing Gen AI’s impact on software coding.