Enhancing Differential Testing: LLM-Powered Automation in Release Engineering
This program is tentative and subject to change.
In modern software engineering, efficient release engineering workflows are essential for quickly delivering new features to production. This not only improves company productivity but also provides customers with frequent updates, which can lead to increased profits. At Microsoft, we collaborated with the Identity and Network Access (IDNA) team to automate their release engineering workflows. They use differential testing to classify differences between test and production environments, which helps them assess how new changes perform with real-world traffic before pushing updates to production. This process enhances resiliency and ensures robust changes to the system. However, on-call engineers (OCEs) must manually label hundreds or thousands of behavior differences, which is time-consuming. In this work, we present a method leveraging Large Language Models (LLMs) to automate the classification of these differences, which saves OCEs a significant amount time. Our experiments demonstrate that LLMs are effective classifiers for automating the task of behavior difference classification, which can lead to speeding up release workflows, and improved OCE productivity.
This program is tentative and subject to change.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | |||
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. |