9 citations · 11 across the 13 of their papers we have counts for
3 papers · 1 filter
Factoring Expertise, Workload, and Turnover into Code Review Recommendation
Fahimeh Hajari, Samaneh Malmir, Ehsan Mirsaeedi +1
Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turno…
Improving Code Reviewer Recommendation: Accuracy, Latency, Workload, and Bystanders
Peter C. Rigby, Seth Rogers, Sadruddin Saleem +5
The code review team at Meta is continuously improving the code review process. To evaluate the new recommenders, we conduct three A/B tests which are a type of randomized controll…
AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation
Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad +6
Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we pres…