Designing software the world can depend on

Selected Papers

IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models

FSE '25

µPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults

ICSE '25

An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization

ICSE '25

Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning

ICSE '25

Leveraging Data Characteristics for Bug Localization in Deep Learning Programs

TOSEM '24

Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment

ICSE '24

Fairify: Fairness Verification of Neural Networks

ICSE '23

Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement

ICSE '23

Towards Understanding Fairness and its Composition in Ensemble Machine Learning

ICSE '23

23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software

ESEC-FSE '22

The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large

ICSE '22

Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules

ICSE '22

DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs

ICSE '22

Manas: Mining Software Repositories to Assist AutoML

ICSE '22

Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline

ESEC-FSE '21

On Decomposing a Deep Neural Network into Modules

ESEC-FSE '20

Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness

ESEC-FSE '20

Repairing Deep Neural Networks Fix Patterns and Challenges

ICSE '20

BCFA Bespoke Control Flow Analysis for CFA at Scale

ICSE '20

A Comprehensive Study on Deep Learning Bug Characteristics

ESEC-FSE '19

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