133 citations · 150 across the 12 of their papers we have counts for
7 papers · 1 filter
Fuzzing with Agents? Generators Are All You Need
Vasudev Vikram, Rohan Padhye
Modern generator-based fuzzing techniques combine lightweight input generators with coverage-guided mutation as a method of exploring deep execution paths in a target program. A co…
On the Freshness of Pinned Dependencies in Maven
Vasudev Vikram, Yuvraj Agarwal, Rohan Padhye
Library dependencies in software ecosystems play a crucial role in the development of software. As newer releases of these libraries are published, developers may opt to pin their…
Software Engineering Methods For AI-Driven Deductive Legal Reasoning
Rohan Padhye
The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law.…
Can Large Language Models Write Good Property-Based Tests?
Vasudev Vikram, Caroline Lemieux, Joshua Sunshine +1
Property-based testing (PBT), while an established technique in the software testing research community, is still relatively underused in real-world software. Pain points in writin…
Efficient Fuzz Testing for Apache Spark Using Framework Abstraction
Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar +2
The emerging data-intensive applications are increasingly dependent on data-intensive scalable computing (DISC) systems, such as Apache Spark, to process large data. Despite their…
Growing a Test Corpus with Bonsai Fuzzing
Vasudev Vikram, Rohan Padhye, Koushik Sen
This paper presents a coverage-guided grammar-based fuzzing technique for automatically generating a corpus of concise test inputs for programs such as compilers. We walk-through a…
Semantic Fuzzing with Zest
Rohan Padhye, Caroline Lemieux, Koushik Sen +2
Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed in…