Using Reinforcement Learning to Sustain the Performance of Version Control Repositories
Although decentralized Version Control Systems (VCSs) like Git support several organizational structures, a central copy of the repository is typically where development activity is coalesced and where official software releases are produced. Due to growth in team size and the popularity of monolithic repositories (a.k.a., “monorepos”) that span entire organizations, central repositories are being strained. Remedial actions that devops engineers take, such as performing garbage collection routines, can backfire because they are computationally expensive and if run at an inopportune moment, may degrade repository performance or even cause the host to crash.
To sustain the performance of VCSs under production workloads, we propose a reinforcement learning agent that can take remedial actions. Since a large quantity of VCS activity is needed to train the agent, we first augment the VCS to enable a greater throughput, observing that the augmented VCS outperforms the stock VCS to a large, statistically significant degree. Then, we compare the performance that a central VCS can sustain when the agent is applied against a schedule-based garbage collection policy and a no-action baseline, observing 64 to 82-fold improvements in the Area Under the Curve (AUC) that plots repository performance over time. This paper takes a promising first step towards automatically sustaining the performance of VCSs under heavy production workloads.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | AI for ProcessSE In Practice (SEIP) / Demonstrations / New Ideas and Emerging Results (NIER) at 212 Chair(s): Keheliya Gallaba Centre for Software Excellence, Huawei Canada | ||
16:00 15mTalk | OptCD: Optimizing Continuous Development Demonstrations Talank Baral George Mason University, Emirhan Oğul Middle East Technical University, Shanto Rahman The University of Texas at Austin, August Shi The University of Texas at Austin, Wing Lam George Mason University | ||
16:15 15mTalk | LLMs as Evaluators: A Novel Approach to Commit Message Quality Assessment New Ideas and Emerging Results (NIER) Abhishek Kumar Indian Institute of Technology, Kharagpur, Sandhya Sankar Indian Institute of Technology, Kharagpur, Sonia Haiduc Florida State University, Partha Pratim Das Indian Institute of Technology, Kharagpur, Partha Pratim Chakrabarti Indian Institute of Technology, Kharagpur | ||
16:30 15mTalk | Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings SE In Practice (SEIP) Petr Tsvetkov JetBrains Research, Aleksandra Eliseeva JetBrains Research, Danny Dig University of Colorado Boulder, JetBrains Research, Alexander Bezzubov JetBrains, Yaroslav Golubev JetBrains Research, Timofey Bryksin JetBrains Research, Yaroslav Zharov JetBrains Research Pre-print | ||
16:45 15mTalk | Enhancing Differential Testing: LLM-Powered Automation in Release Engineering SE In Practice (SEIP) Ajay Krishna Vajjala George Mason University, Arun Krishna Vajjala George Mason University, Carmen Badea Microsoft Research, Christian Bird Microsoft Research, Robert DeLine Microsoft Research, Jason Entenmann Microsoft Research, Nicole Forsgren Microsoft Research, Aliaksandr Hramadski Microsoft, Sandeepan Sanyal Microsoft, Oleg Surmachev Microsoft, Thomas Zimmermann University of California, Irvine, Haris Mohammad Microsoft, Jade D'Souza Microsoft, Mikhail Demyanyuk Microsoft | ||
17:00 15mTalk | How much does AI impact development speed? An enterprise-based randomized controlled trial SE In Practice (SEIP) Elise Paradis Google, Inc, Kate Grey Google, Quinn Madison Google, Daye Nam Google, Andrew Macvean Google, Inc., Nan Zhang Google, Ben Ferrari-Church Google, Satish Chandra Google, Inc | ||
17:15 15mTalk | Using Reinforcement Learning to Sustain the Performance of Version Control Repositories New Ideas and Emerging Results (NIER) Shane McIntosh University of Waterloo, Luca Milanesio GerritForge Inc., Antonio Barone GerritForge Inc., Jacek Centkowski GerritForge Inc., Marcin Czech GerritForge Inc., Fabio Ponciroli GerritForge Inc. Pre-print |