28 citations · 102 across the 18 of their papers we have counts for
8 papers · 1 filter
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning
Ashwin Kallingal Joshy, Mirza Sanjida Alam, Shaila Sharmin +2
Deep learning vulnerability detection tools are increasing in popularity and have been shown to be effective. These tools rely on large volume of high quality training data, which…
Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection
Benjamin Steenhoek, Md Mahbubur Rahman, Shaila Sharmin +1
Recently, pretrained language models have shown state-of-the-art performance on the vulnerability detection task. These models are pretrained on a large corpus of source code, then…
Towards Causal Deep Learning for Vulnerability Detection
Md Mahbubur Rahman, Ira Ceka, Chengzhi Mao +3
Deep learning vulnerability detection has shown promising results in recent years. However, an important challenge that still blocks it from being very useful in practice is that t…
Reproducing Failures in Fault Signatures
Ashwin Kallingal Joshy, Benjamin Steenhoek, Xiuyuan Guo +1
Software often fails in the field, however reproducing and debugging field failures is very challenging: the failure-inducing input may be missing, and the program setup can be com…
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…
Beryllium: Neural Search for Algorithm Implementations
Adithya Kulkarni, Mohna Chakraborty, Yonas Sium +3
In this paper, we explore the feasibility of finding algorithm implementations from code. Successfully matching code and algorithms can help understand unknown code, provide refere…