I am an incoming Tenure-Track
Assistant Professor in the Department of
Computer Science at Cornell University,
starting Fall 2024!
Prospective students: I am
looking for skilled and motivated undergraduates, PhD
students, and postdocs to join my group. If you are
interested in working with me at the
intersection of Software Engineering and
Machine Learning, please drop me an
email and apply to the
Cornell CS PhD
program.
I am currently a Postdoctoral Researcher in
the
Computer &
Information Science Department at the
University of Pennsylvania, working with
Mayur Naik. I obtained my
PhD in Computer Science from the
University of Illinois
Urbana-Champaign in 2023.
You can find my CV
here.
My research interests are at the
intersection of Software Engineering and
Machine Learning. I am particularly
interested in 1) developing novel
techniques and tools to improve the
reliability of Machine Learning-based
systems, and 2) leveraging Machine Learning
to address challenging tasks in Software
Engineering.
My research focuses on following themes:
Apart from these topics, I also developed
novel inference algorithms and robustness
analyses for probabilistic programming
[UAI 2023] [ATVA 2021].
FLEX: Fixing Flaky Tests in
Machine-Learning Projects by
Updating Assertion Bounds
29th ACM Joint European Software Engineering Conference and Symposium on the
Foundations of Software
Engineering (FSE
2021)
Saikat Dutta, August Shi, and Sasa Misailovic
TERA: Optimizing Stochastic Regression Tests in Machine Learning Projects
30th ACM SIGSOFT International Symposium on Software Testing and Analysis
(ISSTA
2021)
Saikat Dutta, Jeeva
Selvam, Aryaman Jain, and Sasa Misailovic
Testing Probabilistic Programming Systems
26th ACM Joint European Software Engineering Conference and Symposium on the
Foundations of Software Engineering (FSE 2018)
Saikat Dutta, Owolabi Legunsen, Zixin
Huang, Sasa Misailovic
2023
Randomness-Aware Testing of Machine Learning-based Systems
Ph.D. Dissertation, University of Illinois Urbana-Champaign, July 2023
Saikat Dutta
ASTRA: Understanding the Practical Impact of Robustness for Probabilistic Programs
39th Conference on Uncertainty in Artificial Intelligence (UAI 2023)
Zixin Huang, Saikat Dutta, and Sasa Misailovic
Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests
45th International Conference on Software Engineering (ICSE 2023)
Steven Xia, Saikat Dutta, Sasa Misailovic, Darko Marinov, and Lingming Zhang
2022
To Seed or Not to Seed? An Empirical Analysis of Usage of Seeds for Testing in Machine Learning Projects
15th IEEE International Conference on Software Testing, Verification and Validation (ICST 2022)
Saikat Dutta, Anshul Arunachalam and Sasa Misailovic
InspectJS: Leveraging Code Similarity and User-Feedback for Effective Taint Specification Inference for JavaScript
44th International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP 2022)
Saikat Dutta, Diego Garbervetsky, Shuvendu Lahiri, Max Schäfer
SixthSense: Debugging Convergence Problems in Probabilistic Programs via
Program Representation Learning
25th International Conference on Fundamental Approaches to Software Engineering
(FASE 2022)
Saikat Dutta, Zixin Huang, and Sasa Misailovic
2021
Automated Quantized Inference for Probabilistic Programs with AQUA
Innovations in Systems and Software Engineering: A NASA Journal (ISSE
NASA)
Zixin Huang, Saikat
Dutta, and Sasa
Misailovic
Extended version of our ATVA 2021 paper
AQUA: Automated Quantized
Inference for Probabilistic
Programs
19th International Symposium on
Automated Technology for
Verification and Analysis
(ATVA
2021)
Zixin Huang, Saikat
Dutta, and Sasa
Misailovic
FLEX: Fixing Flaky Tests in
Machine-Learning Projects by
Updating Assertion Bounds
29th ACM Joint European Software Engineering Conference and Symposium on the
Foundations of Software
Engineering (FSE
2021)
Saikat Dutta, August Shi, and Sasa Misailovic
TERA: Optimizing Stochastic Regression Tests in Machine Learning Projects
30th ACM SIGSOFT International Symposium on Software Testing and Analysis
(ISSTA
2021)
Saikat Dutta, Jeeva
Selvam, Aryaman Jain, and Sasa Misailovic
2020
Detecting Flaky Tests in Probabilistic and Machine Learning Applications
29th ACM SIGSOFT International Symposium on Software Testing and Analysis
(ISSTA 2020)
Saikat Dutta, August Shi, Rutvik Choudhary, Zhekun Zhang, Aryaman Jain,
and Sasa Misailovic
2019
Storm: Program Reduction for Testing and Debugging Probabilistic Programming
Systems
27th ACM Joint European Software Engineering Conference and Symposium on the
Foundations of Software Engineering (FSE 2019)
Saikat Dutta, Wenxian Zhang, Zixin
Huang, Sasa Misailovic
2018
Testing Probabilistic Programming Systems
26th ACM Joint European Software Engineering Conference and Symposium on the
Foundations of Software Engineering (FSE 2018)
Saikat Dutta, Owolabi Legunsen, Zixin
Huang, Sasa Misailovic
2013-17
AutoSense: A Framework for Automated Sensitivity Analysis of Program Data
IEEE Transactions on Software Engineering (TSE 2017)
Bernard Nongpoh, Rajarshi Ray, Saikat
Dutta, Ansuman Banerjee
Enhancing branch prediction using software evolution
10th IEEE International Conference on Networking, Architecture, and Storage
(NAS 2015)
Saikat Dutta, Moumita Das, Ansuman
Banerjee
A New Approach for Minimal Environment Construction for Modular Property
Verification
24th Asian Test Symposium (ATS 2015)
Saikat Dutta, Soumi Chattopadhyay, Ansuman Banerjee, Pallab Dasgupta
A Framework for Fast Service Verification and Query Execution for Boolean
Service Rules>
9th Asia-Pacific Services Computing Conference (APSCC 2015)
Soumi Chattopadhyay, Saikat Dutta,
Ansuman Banerjee
Daikon to Prioritize and Group Unit Bugs
Formal Aspects of Component Software - 10th International Symposium (FACS
2013)
Nehul Jain, Saikat Dutta, Ansuman
Banerjee, Anil K. Ghosh, Lihua Xu, Huibiao
Zhu