I am an applied ML and software engineering researcher at Meta, working on developing AI-based solutions to industry-scale software engineering problems. My research aims to address practical problems in software development, such as code generation/synthesis, code review, bug attribution, root cause analysis, etc. using machine learning solutions. The projects I have worked on have found significant industrial impact at Meta.

Publications

1. Moving Faster and Reducing Risk: Using LLMs in Release Deployment
   Rui Abreu, Vijayaraghavan Murali, Peter C Rigby, Chandra Maddila, Weiyan Sun, Jun Ge, Kaavya Chinniah, Audris Mockus, Megh Mehta, Nachiappan Nagappan
   (arxiv)
2. AI-Assisted SQL Authoring at Industry Scale
   Chandra Maddila, Negar Ghorbani, Kosay Jabre, Vijayaraghavan Murali, Edwin Kim, Parth Thakkar, Nikolay Pavlovich Laptev, Olivia Harman, Diana Hsu, Rui Abreu, Peter C. Rigby
   (arxiv)
3. Multi-line AI-assisted Code Authoring
   Omer Dunay, Daniel Cheng, Adam Tait, Parth Thakkar, Peter C. Rigby, Andy Chiu, Imad Ahmad, Arun Ganesan, Chandra Maddila, Vijayaraghavan Murali, Ali Tayyebi, Nachiappan Nagappan
   FSE 2024 Industry Track (arxiv)
4. AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation
   Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad, Michael Bolin, Daniel Cheng, Negar Ghorbani, Renuka Fernandez, Nachiappan Nagappan, Peter C. Rigby
   FSE 2024 (arxiv) - Highlight by Meta CEO Mark Zuckerberg FB/IG
5. Learning to Learn to Predict Performance Regressions in Production at Meta
   Moritz Beller, Hongyu Li, Vivek Nair, Vijayaraghavan Murali, Imad Ahmad, Jürgen Cito, Drew Carlson, Gareth Ari Aye, Wes Dyer
   AST 2023
6. Detecting Privacy-Sensitive Code Changes with Language Modeling
   Gokalp Demirci, Vijayaraghavan Murali, Imad Ahmad, Rajeev Rao, Gareth Ari Aye
   MSR 2022 Industry Track
7. Counterfactual Explanations for Models of Code
   Jurgen Cito, Isil Dillig, Vijayaraghavan Murali, Satish Chandra
   ICSE SEIP 2022
8. Improving Code Autocompletion with Transfer Learning
   Wen Zhou, Sonia Kim, Vijayaraghavan Murali, Ari Aye
   ICSE SEIP 2022
9. Explaining mispredictions of machine learning models using rule induction
   Jurgen Cito, Isil Dillig, Sonia Kim, Vijayaraghavan Murali, Satish Chandra
   FSE 2021
10. Industry-scale IR-based Bug Localization: A Perspective from Facebook
    Vijayaraghavan Murali, Lee Gross, Rebecca Qian, Satish Chandra
    ICSE SEIP 2021 - Distinguished Paper award
11. Scalable Statistical Root Cause Analysis on App Telemetry
    Vijayaraghavan Murali, Edward Yao, Umang Mathur, Satish Chandra
    ICSE SEIP 2021
12. Scaffle: bug localization on millions of files
    Michael Pradel, Vijayaraghavan Murali, Rebecca Qian, Mateusz Machalica, Erik Meijer, Satish Chandra
    ISSTA 2020
13. Debugging crashes using continuous contrast set mining
    Rebecca Qian, Yang Yu, Wonhee Park, Vijayaraghavan Murali, Stephen Fink, Satish Chandra
    ICSE SEIP 2020
14. Neural query expansion for code search
    Jason Liu, Sonia Kim, Vijayaraghavan Murali, Swarat Chaudhuri, Satish Chandra
    MAPL 2019
15. Programmatically Interpretable Reinforcement Learning
    Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri
    ICML 2018
16. Neural Sketch Learning for Conditional Program Generation
    Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
    ICLR 2018
17. Abridging Source Code
    Binhang Yuan, Vijayaraghavan Murali, Chris Jermaine
    OOPSLA 2017
18. Bayesian Specification Learning for Finding API Usage Errors
    Vijayaraghavan Murali, Swarat Chaudhuri, Chris Jermaine
    FSE 2017
19. A Path-Sensitively Sliced Control Flow Graph
    Joxan Jaffar, Vijayaraghavan Murali
    FSE 2014
20. Lazy Symbolic Execution for Enhanced Learning
    Duc-Hiep Chu, Joxan Jaffar, Vijayaraghavan Murali
    RV 2014
21. A Hybrid Algorithm for Error Trace Explanation
    Vijayaraghavan Murali, Nishant Sinha, Emina Torlak, Satish Chandra
    VSTTE 2014
22. Boosting Concolic Testing via Interpolation
    Joxan Jaffar, Vijayaraghavan Murali, Jorge A. Navas
    FSE 2013
23. Path-Sensitive Backward Slicing
    Joxan Jaffar, Vijayaraghavan Murali, Jorge A. Navas, Andrew E. Santosa
    SAS 2012
24. TRACER: A Symbolic Execution Tool for Verification
    Joxan Jaffar, Vijayaraghavan Murali, Jorge A. Navas, Andrew E. Santosa
    CAV 2012
25. Towards predictive modeling of message passing communication
    Verdi March, Vijayaraghavan Murali, Yong Meng Teo, Simon See, James T. Himer
    HPCC 2009

Blog Posts

• Minesweeper automates root cause analysis as a first-line defense against bugs
• CCSM: Scalable statistical anomaly detection to resolve app crashes faster

Service

• PLDI 2022
• ISSTA 2020
• CAV 2019
• POPL 2015 (Artifact Committee)
• FSE 2014 (Artifact Committee)

Bio

I have been a software engineer at Meta (Facebook) since 2018, where I work in developer infrastructure. Before, I was a Research Scientist (postdoc) at Rice University from 2015-2018, working with Swarat Chaudhuri and Chris Jermaine on the Pliny project. Prior to that, I was a Ph.D. student at the National University of Singapore (NUS) from 2009-2014, advised by Joxan Jaffar. I interned at IBM Research India during my Ph.D., working with Satish Chandra. My Ph.D. background is originally in programming languages and formal methods, and I became familiar with machine learning during my postdoc at Rice. I obtained my B.Computing also from NUS in 2009.

My hometown is Chennai in south India. Outside research, I enjoy video games, backyard gardening, and traveling.