15 citations · 47 across the 9 of their papers we have counts for
10 papers
How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs
Ali Alfageeh, Rahul Gopinath, Amin Alipour
Prompts are the primary mechanism for directing the behavior of large language models (LLMs). Yet the internal structure and causal hierarchy of prompts remain poorly understood: w…
Systematic Assessment of Fuzzers using Mutation Analysis
Philipp Görz, Björn Mathis, Keno Hassler +4
Fuzzing is an important method to discover vulnerabilities in programs. Despite considerable progress in this area in the past years, measuring and comparing the effectiveness of f…
Input Repair via Synthesis and Lightweight Error Feedback
Lukas Kirschner, Ezekiel Soremekun, Rahul Gopinath +1
Often times, input data may ostensibly conform to a given input format, but cannot be parsed by a conforming program, for instance, due to human error or data corruption. In such c…
Mutation Analysis: Answering the Fuzzing Challenge
Rahul Gopinath, Philipp Görz, Alex Groce
Fuzzing is one of the fastest growing fields in software testing. The idea behind fuzzing is to check the behavior of software against a large number of randomly generated inputs,…
FormatFuzzer: Effective Fuzzing of Binary File Formats
Rafael Dutra, Rahul Gopinath, Andreas Zeller
Effective fuzzing of programs that process structured binary inputs, such as multimedia files, is a challenging task, since those programs expect a very specific input format. Exis…
Using Relative Lines of Code to Guide Automated Test Generation for Python
Josie Holmes, Iftekhar Ahmed, Caius Brindescu +3
Raw lines of code (LOC) is a metric that does not, at first glance, seem extremely useful for automated test generation. It is both highly language-dependent and not extremely mean…
Fuzzing with Fast Failure Feedback
Rahul Gopinath, Bachir Bendrissou, Björn Mathis +1
Fuzzing -- testing programs with random inputs -- has become the prime technique to detect bugs and vulnerabilities in programs. To generate inputs that cover new functionality, fu…
Inferring Input Grammars from Dynamic Control Flow
Rahul Gopinath, Björn Mathis, Andreas Zeller
A program is characterized by its input model, and a formal input model can be of use in diverse areas including vulnerability analysis, reverse engineering, fuzzing and software t…
Building Fast Fuzzers
Rahul Gopinath, Andreas Zeller
Fuzzing is one of the key techniques for evaluating the robustness of programs against attacks. Fuzzing has to be effective in producing inputs that cover functionality and find vu…
Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing
Rahul Gopinath, Björn Mathis, Mathias Höschele +2
Generating valid test inputs for a program is much easier if one knows the input language. We present first successes for a technique that, given a program P without any input samp…