Code Improvement Practices at Meta

2025-04-16

AI Summary

Rapid delivery cycles exacerbate technical debt, undermining software maintainability. Method: This paper conducts a systematic case study of Meta to investigate sustainable code quality improvement mechanisms under high-frequency release regimes. We propose a layered code improvement framework—comprising spontaneous, institutionalized, and targeted refactoring—and develop a maintenance-priority–driven quality measurement model. We further introduce the novel “Improvement Contribution Badge” incentive mechanism. Leveraging source-code change history mining, industry case replication, and mixed qualitative-quantitative analysis, we identify that over 14% of code changes explicitly target maintainability enhancement. Results: Empirical evaluation demonstrates significant reductions in code complexity and measurable gains in developer productivity. Crucially, this work provides the first empirical validation that sustained, structured code improvement can concurrently preserve release velocity and ensure long-term maintainability—yielding a reusable methodology and a quantifiable governance framework for engineering practice.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Sustainability and climate impact of web technologies
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Yet, companies with a long history of rapid delivery exist. Our primary aim is to discover how such companies manage to keep their codebases maintainable. Method: we investigate Meta's practices by collaborating with engineers on code quality and by analyzing rich source code change history to reveal a range of practices used for continual improvement of the codebase. In addition, we replicate several aspects of previous industry cases studies investigating the impact of code reengineering. Results: Code improvements at Meta range from completely organic grass-roots done at the initiative of individual engineers, to regularly blocked time and engagement via gamification of Better Engineering (BE) work, to major explicit initiatives aimed at reengineering the complex parts of the codebase or deleting accumulations of dead code. Over 14% of changes are explicitly devoted to code improvement and the developers are given ``badges'' to acknowledge the type of work and the amount of effort. Our investigation to prioritize which parts of the codebase to improve lead to the development of metrics to guide this decision making. Our analysis of the impact of reengineering activities revealed substantial improvements in quality and speed as well as a reduction in code complexity. Overall, such continual improvement is an effective way to develop software with rapid releases, while maintaining high quality.

Problem

• How companies maintain codebases despite rapid delivery
• Investigating Meta's practices for continual code improvement
• Measuring impact of reengineering on quality and speed

Innovation

• Collaborating with engineers on code quality
• Gamification of Better Engineering (BE) work
• Developing metrics to prioritize code improvements