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DPE Summit 2024: Measuring Software Engineering Productivity - Stanford Research on Software Engineering Productivity

Date

Sept 2024

In this session, Simon Obstbaum and Yegor Denisov-Blanch presented research from Stanford University that reexamines how software engineering productivity is measured. The discussion explored the limitations of existing metrics and proposed a data-driven framework to support better decision-making in software organizations.

  • The proposed new approach is based on extensive research conducted at Stanford University.

  • Effective metrics require reliable data collection to inform decision-making processes.

  • The session discusses the importance of actionable data reporting tools, such as Develocity Reporting & Visualization, for tracking productivity across projects.

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Engineering excellence remained a critical focus for Financial Services Institutions aiming to stay competitive and innovative in an ever-evolving landscape. With the rise of GenAI, there was growing interest in understanding how it could be leveraged to enhance the software development lifecycle and improve engineering productivity.

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Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.

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