About Me

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 and design systems for expressive and reliable programming.

Research Interest

I am interested in Programming Languages and Software Engineering, specifically, in tools for program synthesis and analysis. Currently, my work is motivated by abstractions for programs that aid systems to reason about code by introducing either structure (graphs) or semantics (language constructs). This interest naturally touches language models (LLM) and ML4Code as means to learn/use these abstractions.

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

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


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!


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
ICLR 2024 @ LLMAgents Workshop

paper / code / website

LM Assertions: Computational Constraints for Self-Refining LM Pipelines
Arnav Singhvi*, Manish Shetty*, Shangyin Tan*, Chris Potts, Koushik Sen, Matei Zaharia, Omar Khattab
Under Submission, 2024.

preprint / code / tweet-1 / tweet-2 / blog

CodeScholar: Growing Idiomatic Code Examples
Manish Shetty, Koushik Sen, Ion Stoica
arXiv, 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


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



UC Berkeley:



→ $ Turn any GitHub repo into a dynamic environment for programming agents.
→ $ Growing programs graphs idiomatically for API usage examples.
pat, yappy, & sast
→ $ A new suite of program analysis tools for Python over AST, CFG, and PDGs.