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

2025-02-21

AI Summary

This study investigates the sociological origins of engineers’ sense of responsibility for code quality in code review and how this responsibility evolves under LLM-assisted review. Employing a two-phase qualitative methodology—16 semi-structured interviews and contextualized focus group experiments comparing traditional peer review with LLM-augmented review—the study identifies four intrinsic responsibility drivers: personal quality standards, professional ethics, pride in code quality, and reputation maintenance. It further reveals that conventional peer review fosters a shift from individual to collective accountability through interpersonal, reciprocal accountability; however, LLM integration disrupts this reciprocity, thereby undermining the integrity of collective accountability. The findings underscore that embedding AI into code review requires balancing technical efficacy with the preservation of socio-technical accountability structures. This work contributes both theoretical insights into human-AI collaboration in software engineering and practical implications for designing responsible, socially aware AI-augmented development workflows.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Explores intrinsic drivers of accountability in code reviews.
• Assesses impact of LLMs on accountability in software engineering.
• Examines transition from individual to collective accountability.

Innovation

• LLM-assisted code reviews
• two-phased qualitative study
• preserving collective accountability