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 to quickly analyze and synthesize programs.
I am interested in Programming Languages and Software Engineering, specifically, in techniques for efficient and reliable program analysis and synthesis. Currently my work draws from the connections between programs and graphs, and combines ideas from graph theory and representation learning to study programs and their automated synthesis.
Previously, I worked at Microsoft Research (MSR) with Chetan Bansal, Suman Nath, and Tom Zimmermann on developer and debugging tools for cloud reliability.
If you’d like to collaborate, drop me an email at: manishs@berkeley.edu
CodeScholar: Growing Idiomatic Code Examples
Manish Shetty, Koushik Sen, Ion Stoica
Under submission.
preprint /
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 /
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