Empirical Software Engineering is the study of what actually works in programming. Instead of trusting our instincts we collect data, run studies, and peer-review our results. This talk is all about how we empirically find the facts in software and some of the challenges we face, with a particular focus on software defects and productivity. Talk doesn’t seem to be online yet; in the meantime, you can see a recording of an older version of the talk here.
Software Engineering Productivity Research - Home
The Bulk of Software Engineering is Just Plumbing
I read a post online recently about how some software engineer was asked to complete a technically complex, jargon-filled coding challenge over the...
Laws of Software Engineering
A collection of principles and patterns that shape software systems, teams, and decisions.
The Next Two Years of Software Engineering
Exploring five critical questions shaping software engineering through 2026, with contrasting scenarios for each. These lenses help prepare for the evolving ...
Test-Driven Approaches to Software Engineering with Large Language...
Tests increasingly participate in the decisions made by large language models and software engineering agents. They specify intended behavior, guide program construction and repair, select...
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
Breaking Smart: How Software is Eating the World
21 essays on the software revolution — how agile, networked approaches are disrupting industries and remaking society. In English, German, and French.
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.