7 citations · 14 across the 5 of their papers we have counts for
8 papers
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova +4
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid rob…
Data-Driven Evidence-Based Syntactic Sugar Design
David OBrien, Robert Dyer, Tien N. Nguyen +1
Programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language…
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
Shibbir Ahmed, Hongyang Gao, Hridesh Rajan
Deep learning models are trained with certain assumptions about the data during the development stage and then used for prediction in the deployment stage. It is important to reaso…
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…