97 citations · 379 across the 26 of their papers we have counts for
5 papers · 1 filter
Mutation-based Fault Localization of Deep Neural Networks
Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad +1
Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, inclu…
What Kinds of Contracts Do ML APIs Need?
Samantha Syeda Khairunnesa, Shibbir Ahmed, Sayem Mohammad Imtiaz +2
Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document A…
An Effective Data-Driven Approach for Localizing Deep Learning Faults
Mohammad Wardat, Breno Dantas Cruz, Wei Le +1
Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their s…
Characterizing Bugs in Python and R Data Analytics Programs
Shibbir Ahmed, Mohammad Wardat, Hamid Bagheri +2
R and Python are among the most popular languages used in many critical data analytics tasks. However, we still do not fully understand the capabilities of these two languages w.r.…
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
Giang Nguyen, Sumon Biswas, Hridesh Rajan
Machine learning (ML) is increasingly being used in critical decision-making software, but incidents have raised questions about the fairness of ML predictions. To address this iss…