32 citations · 37 across the 5 of their papers we have counts for
6 papers
Vulnerability Mimicking Mutants
Aayush Garg, Renzo Degiovanni, Mike Papadakis +1
With the increasing release of powerful language models trained on large code corpus (e.g. CodeBERT was trained on 6.4 million programs), a new family of mutation testing tools has…
Assertion Inferring Mutants
Aayush Garg, Renzo Degiovanni, Facundo Molina +4
Specification inference techniques aim at (automatically) inferring a set of assertions that capture the exhibited software behaviour by generating and filtering assertions through…
Learning from what we know: How to perform vulnerability prediction using noisy historical data
Aayush Garg, Renzo Degiovanni, Matthieu Jimenez +3
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classif…
Syntactic Vs. Semantic similarity of Artificial and Real Faults in Mutation Testing Studies
Milos Ojdanic, Aayush Garg, Ahmed Khanfir +3
Fault seeding is typically used in controlled studies to evaluate and compare test techniques. Central to these techniques lies the hypothesis that artificially seeded faults invol…
Cerebro: Static Subsuming Mutant Selection
Aayush Garg, Milos Ojdanic, Renzo Degiovanni +3
Mutation testing research has indicated that a major part of its application cost is due to the large number of low utility mutants that it introduces. Although previous research h…
Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data
Aayush Garg, Renzo Degiovanni, Matthieu Jimenez +3
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classif…