4 citations · 8 across the 5 of their papers we have counts for
5 papers
A Survey of Trojans in Neural Models of Source Code: Taxonomy and Techniques
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +4
In this work, we study literature in Explainable AI and Safe AI to understand poisoning of neural models of code. In order to do so, we first establish a novel taxonomy for Trojan…
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Aftab Hussain, Md Rafiqul Islam Rabin, Bowen Xu +2
Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the…
Study of Distractors in Neural Models of Code
Md Rafiqul Islam Rabin, Aftab Hussain, Sahil Suneja +1
Finding important features that contribute to the prediction of neural models is an active area of research in explainable AI. Neural models are opaque and finding such features sh…
DIAR: Removing Uninteresting Bytes from Seeds in Software Fuzzing
Aftab Hussain, Mohammad Amin Alipour
Software fuzzing mutates bytes in the test seeds to explore different behaviors of the program under test. Initial seeds can have great impact on the performance of a fuzzing campa…
FMViz: Visualizing Tests Generated by AFL at the Byte-level
Aftab Hussain, Mohammad Amin Alipour
Software fuzzing is a strong testing technique that has become the de facto approach for automated software testing and software vulnerability detection in the industry. The random…