7 citations · 14 across the 6 of their papers we have counts for
6 papers
Leveraging Data Characteristics for Bug Localization in Deep Learning Programs
Ruchira Manke, Mohammad Wardat, Foutse Khomh +1
Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of applications. Like any software system, DL programs can have bugs. To support bug lo…
Enhanced LLM-Based Framework for Predicting Null Pointer Dereference in Source Code
Md. Fahim Sultan, Tasmin Karim, Md. Shazzad Hossain Shaon +2
Software security is crucial in any field where breaches can exploit sensitive data, and lead to financial losses. As a result, vulnerability detection becomes an essential part of…
A Combined Feature Embedding Tools for Multi-Class Software Defect and Identification
Md. Fahim Sultan, Tasmin Karim, Md. Shazzad Hossain Shaon +2
In software, a vulnerability is a defect in a program that attackers might utilize to acquire unauthorized access, alter system functions, and acquire information. These vulnerabil…
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…
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…
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Mohammad Wardat, Breno Dantas Cruz, Wei Le +1
Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that…