Boa meets python: a boa dataset of data science software in python language. - Sumon Biswas; Md Johirul Islam; Yijia Huang; Hridesh Rajan (MSR, 2019)

Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. - Sumon Biswas; Hridesh Rajan (ESEC/SIGSOFT FSE, 2020)

Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline. - Sumon Biswas; Hridesh Rajan (ESEC/SIGSOFT FSE, 2021)

The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. - Sumon Biswas; Mohammad Wardat; Hridesh Rajan (ICSE, 2022)

23 shades of self-admitted technical debt: an empirical study on machine learning software. - David O'Brien; Sumon Biswas; Sayem Imtiaz; Rabe Abdalkareem; Emad Shihab; Hridesh Rajan (ESEC/SIGSOFT FSE, 2022)

An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models. - Rangeet Pan; Sumon Biswas; Mohna Chakraborty; Breno Dantas Cruz; Hridesh Rajan (CoRR, 2022)

Fairify: Fairness Verification of Neural Networks. - Sumon Biswas; Hridesh Rajan (ICSE, 2023)

Towards Understanding Fairness and its Composition in Ensemble Machine Learning. - Usman Gohar; Sumon Biswas; Hridesh Rajan (ICSE, 2023)

Towards Safe ML-Based Systems in Presence of Feedback Loops. - Sumon Biswas; Yining She; Eunsuk Kang (SE4SafeML@SIGSOFT FSE, 2023)

Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML. - Giang Nguyen; Sumon Biswas; Hridesh Rajan (ESEC/SIGSOFT FSE, 2023)

Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot. - David O'Brien; Sumon Biswas; Sayem Mohammad Imtiaz; Rabe Abdalkareem; Emad Shihab; Hridesh Rajan (ICSE, 2024)

FairSense: Long-Term Fairness Analysis of ML-Enabled Systems. - Yining She; Sumon Biswas; Christian Kästner; Eunsuk Kang (ICSE, 2025)