Saikat Dutta
Saikat Dutta
saikatd2 [at] illinois [dot] edu
Room 3107
Thomas M Siebel Center
201 Goodwin Avenue
Urbana
Illinois, 61801
United States
About Me
I am a PhD candidate in the Computer Science Department at University of Illinois, Urbana-Champaign. I am currently being advised by Prof. Sasa Misailovic. My primary research interests are in Software Engineering, with a focus on Sofware Testing and Machine Learning. I develop novel testing techniques and tools to improve the reliability of Machine Learning-based systems. My research focuses on 1) automatically detecting bugs and debugging failures in ML libraries and 2) detecting bugs more effectively and efficiently by improving the quality of developer-written tests in ML libraries.

News:
I am on the Academic Job Market. Please reach out if you think I will be a good fit for your department.

Application Materials: [CV] [Research] [Teaching] [Diversity]
Awards
Mavis Future Faculty Fellowship 2022-23
Facebook PhD Fellowship 2020-22
3M Foundation Fellowship 2019-2020
Selected Publications
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper] [Code]
All Publications
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper] [Slides]
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
[Paper] [Code]
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
[Paper]
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper] [Code]
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
[Paper]
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
Service
TSE 2022 Reviewer
MSR Shadow PC 2022
PLDI 2021 Artifact Evaluation Committee
OOPSLA 2020 Artifact Evaluation Committee
News
  • New:
    Our paper Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests has been accepted to ICSE 2023!
  • Our paper To Seed or Not to Seed? An Empirical Analysis of Usage of Seeds for Testing in Machine Learning Projects has been accepted to ICST 2022!
  • Our paper InspectJS: Leveraging Code Similarity and User-Feedback for Effective Taint Specification Inference for JavaScript has been accepted to ICSE-SEIP 2022!
  • Our paper SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning has been accepted to FASE 2022!
  • Our paper AQUA: Automated Quantized Inference for Probabilistic Programs has been accepted to ATVA 2021!
  • Our paper TERA: Optimizing Stochastic Regression Tests in Machine Learning Projects has been accepted to ISSTA 2021!
  • Our paper FLEX: Fixing Flaky Tests in Machine-Learning Projects by Updating Assertion Bounds has been accepted to ESEC/FSE 2021!
  • I will be interning at Amazon Web Services (AWS) with the Automated Reasoning Group (ARG) for Summer 2021!
  • Our paper on Detecting Flaky Tests in Probabilistic and Machine Learning Applications was accepted to ISSTA 2020!
  • I will be interning at Microsoft Research, Redmond with the RISE group for Summer 2020!
    Looking forward to it!
  • Awarded Facebook PhD Fellowship 2020
    Thanks Facebook!
  • Our paper, Storm: Program Reduction for Testing and Debugging Probabilistic Programming Systems, has been accepted to FSE 2019
  • Selected for 3M Foundation Fellowship 2018-19
  • Our paper on ProbFuzz, "Testing Probabilistic programming systems" has been accepted to FSE 2018
  • Attended PLDI 2018 at Philadelphia, USA (20-22 June, 2018)
  • Our recent work on Automated Sensitivity Analysis was published in IEEE TSE Volume 43, Issue 12
  • Attended Midwest Programming Language Summit 2017 at Bloomington, Indiana
  • Attended Automated Software Engineering Conference (ASE 2017) at UIUC