Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs

TL;DR

The paper investigates what drives software engineers' accountability for code quality and how social code-review dynamics shape this accountability, particularly when integrating AI. It uses a two-phase qualitative design (interviews then focus groups) to identify intrinsic drivers and to compare peer-led versus LLM-assisted reviews, revealing a shift from individual to collective accountability during code reviews. Four intrinsic drivers—personal standards, professional integrity, pride in code quality, and reputation—underpin individual accountability, with code review serving as a mechanism that fosters collective ownership. Introducing LLMs disrupts this social process by breaking reciprocity and social validation, though AI can educate and filter obvious issues; effective AI integration must preserve the social fabric and collective accountability that underpins code quality in SE.

Abstract

Accountability is an innate part of social systems. It maintains stability and ensures positive pressure on individuals' decision-making. As actors in a social system, software developers are accountable to their team and organization for their decisions. However, the drivers of accountability and how it changes behavior in software development are less understood. In this study, we look at how the social aspects of code review affect software engineers' sense of accountability for code quality. Since software engineering (SE) is increasingly involving Large Language Models (LLM) assistance, we also evaluate the impact on accountability when introducing LLM-assisted code reviews. We carried out a two-phased sequential qualitative study (interviews -> focus groups). In Phase I (16 interviews), we sought to investigate the intrinsic drivers of software engineers influencing their sense of accountability for code quality, relying on self-reported claims. In Phase II, we tested these traits in a more natural setting by simulating traditional peer-led reviews with focus groups and then LLM-assisted review sessions. We found that there are four key intrinsic drivers of accountability for code quality: personal standards, professional integrity, pride in code quality, and maintaining one's reputation. In a traditional peer-led review, we observed a transition from individual to collective accountability when code reviews are initiated. We also found that the introduction of LLM-assisted reviews disrupts this accountability process, challenging the reciprocity of accountability taking place in peer-led evaluations, i.e., one cannot be accountable to an LLM. Our findings imply that the introduction of AI into SE must preserve social integrity and collective accountability mechanisms.

Figures (3)

• Figure 1: The Dynamics of Accountability in Social Systems
• Figure 2: A summary of our research process.
• Figure 3: The process of accountability for code quality combining Phase I & II findings.