Manish Shetty

I am a PhD student at UC Berkeley in the Programming Systems group and the Sky Lab advised by Koushik Sen. I broadly research techniques at the intersection of PL/SE and AI.

My research involves improving the capabilities and reliability of large-language models for software engineering tasks. I approach this as (1) constructing model evaluations and (2) introducing symbolic techniques like search. A key piece of this is breathing life into static codebases, enabling program analysis and execution awareness.

Previously, I worked at Microsoft Research (MSR) in the Programming Languages and Systems Group on developer and debugging tools for cloud reliability.

If you’d like to collaborate, drop me an email at: manishs@berkeley.edu

News

Apr 2024. 🏆 Honored to have received the Tong Leong Lim Pre-Doctoral Prize at UC Berkeley
Mar 2024. 🏆 Honored to have received the 2024 Outstanding Graduate Student Instructor Award at UC Berkeley
Jan 2024. Interning (returning) with the Systems Innovation group at Microsoft Research in Summer'24! Exploring next-generation AI Assisted System Reliability.
Sept 2023. ✔︎ Passed the Programming Languages Preliminary Exam at UC Berkeley!
May 2023. Taught my first class: CS164: Compilers and Programming Languages at UC Berkeley!
Nov 2022. 🏆 Our empirical study @ Microsoft Research on production incidents in large-scale cloud services received the Best Paper Award 🏆 at SoCC 2022.
Aug 2022. Started my Ph.D. at UC Berkeley advised by Prof. Koushik Sen. Joining the Sky Lab and the Programming Systems group!

Publications

Syzygy: Dual Code-Test C to Rust Translation using LLMs and Dynamic Analysis
Manish Shetty*, Naman Jain*, Adwait Godbole*, Sanjit Seshia, Koushik Sen
LLM4Code Workshop @ ICSE 2025
paper / website

R2E: Turning any GitHub Repository into a Programming Agent Environment
Manish Shetty*, Naman Jain*, Tianjun Zhang, King Han, Koushik Sen, Ion Stoica
ICML 2024: Proceedings of the 41st International Conference on Machine Learning
LLMAgents Workshop @ ICLR 2024
paper / code / website

AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds
Yinfang Chen, Manish Shetty, Gagan Somashekar, Minghua Ma, Yogesh Simmhan, Jonathan Mace, Chetan Bansal, Rujia Wang, and Saravan Rajmohan
MLSys 2025: To appear at the 9th Annual Conference on Machine Learning and Systems
paper / code / website

Building AI Agents for Autonomous Clouds: Challenges and Design Principles
Manish Shetty, Yinfang Chen, Gagan Somashekar, Minghua Ma, Yogesh Simmhan, Xuchao Zhang, Jonathan Mace, Dax Vandevoorde, Pedro Las-Casas, Shachee Mishra Gupta, Suman Nath, Chetan Bansal, Saravan Rajmohan
SoCC 2024: Proceedings of the 14th Symposium on Cloud Computing
paper / featured on Microsoft Research Blog

LM Assertions: Computational Constraints for Self-Refining LM Pipelines
Arnav Singhvi*, Manish Shetty*, Shangyin Tan*, Chris Potts, Koushik Sen, Matei Zaharia, Omar Khattab
Preprint, 2024 (Under Submission)
paper / code / tweet-1 / tweet-2 / blog

CodeScholar: Growing Idiomatic Code Examples
Manish Shetty, Koushik Sen, Ion Stoica
Tech Report, 2023
paper / code

How to Fight Production Incidents? An Empirical Study on a Large-scale Cloud Service
Supriyo Ghosh, Manish Shetty, Chetan Bansal, Suman Nath
SoCC 2022: Proceedings of the 13th Symposium on Cloud Computing
paper / Best Paper Award 🏆

AutoTSG: Learning and Synthesis for Incident Troubleshooting
Manish Shetty, Chetan Bansal, Sai Upadhyayula, Arjun Radhakrishna, Anurag Gupta
FSE 2022: Proceedings of the 30th ACM Symposium on the Foundations of Software Engineering
paper

DeepAnalyze: Learning to Localize Crashes at Scale
Manish Shetty, Chetan Bansal, Suman Nath, Sean Bowles, Henry Wang, Ozgur Arman, Siamak Ahari
ICSE 2022: Proceedings of the 44th International Conference on Software Engineering
paper / slides / talk-1 / talk-2 / talk-3

Neural Knowledge Extraction From Cloud Service Incidents
Manish Shetty, Chetan Bansal, Sumit Kumar, Nikitha Rao, Nachiappan Nagappan, Thomas Zimmermann
ICSE 2021: Proceedings of the 43rd International Conference on Software Engineering
paper / slides / talk / featured on VentureBeat

Awards

Teaching

UC Berkeley:

Service