32 citations · 61 across the 19 of their papers we have counts for
20 papers
How effective are traditional test criteria at detecting bugs in large language models generated code?
Asma Hamidi, Michael Konstantinou, Renzo Degiovanni +1
Test adequacy criteria are widely used to evaluate and guide software testing. Although prior research has extensively examined these criteria using human-written programs, faults,…
Improving Dynamic Specification Inference with LLM-Generated Counterexamples
Agustín Balestra, Agustín Nolasco, Facundo Molina +3
Contract assertions, such as preconditions, postconditions, and invariants, play a crucial role in software development, enabling applications such as program verification, test ge…
How well LLM-based test generation techniques perform with newer LLM versions?
Michael Konstantinou, Renzo Degiovanni, Mike Papadakis
The rapid evolution of Large Language Models (LLMs) has strongly impacted software engineering, leading to a growing number of studies on automated unit test generation. However, t…
Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities
Aayush Garg, Zanis Ali Khan, Renzo Degiovanni +1
Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising capabilities for automating this task.…
YATE: The Role of Test Repair in LLM-Based Unit Test Generation
Michael Konstantinou, Renzo Degiovanni, Jie M. Zhang +2
Recent advances in automated test generation utilises language models to produce unit tests. While effective, language models tend to generate many incorrect tests with respect to…
Bounded Synthesis of Synchronized Distributed Models from Lightweight Specifications
Pablo F. Castro, Luciano Putruele, Renzo Degiovanni +1
We present an approach to automatically synthesize synchronized models from lightweight formal specifications. Our approach takes as input a specification of a distributed system a…
Can we find bugs using LLM-generated oracles?
Michael Konstantinou, Renzo Degiovanni, Mike Papadakis
Unit testing is vital in software development. Typically, a unit test consists of a test prefix and a test oracle which captures the developer's intended behaviour. Traditional tes…
ACoRe: Automated Goal-Conflict Resolution
Luiz Carvalho, Renzo Degiovanni, Matìas Brizzio +4
System goals are the statements that, in the context of software requirements specification, capture how the software should behave. Many times, the understanding of stakeholders o…
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…
Specification Inference for Evolving Systems
Renzo Degiovanni, Facundo Molina, Agustin Nolasco +2
In this paper, we propose an assertion-based approach to capture software evolution, through the notion of commit-relevant specification. A commit-relevant specification summarises…
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…
Efficient Mutation Testing via Pre-Trained Language Models
Ahmed Khanfir, Renzo Degiovanni, Mike Papadakis +1
Mutation testing is an established fault-based testing technique. It operates by seeding faults into the programs under test and asking developers to write tests that reveal these…
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…
BERT: Mutation Testing using Pre-Trained Language Models
Renzo Degiovanni, Mike Papadakis
We introduce BERT, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as in…
Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution
Milos Ojdanic, Ezekiel Soremekun, Renzo Degiovanni +2
When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an expl…
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…
Automated Repair of Unrealisable LTL Specifications Guided by Model Counting
Matías Brizzio, Maxime Cordy, Mike Papadakis +3
The reactive synthesis problem consists of automatically producing correct-by-construction operational models of systems from high-level formal specifications of their behaviours.…
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…
Analyzing Behavioural Scenarios over Tabular Specifications Using Model Checking
Gastón Scilingo, María Marta Novaira, Renzo Degiovanni
Tabular notations, in particular SCR specifications, have proved to be a useful means for formally describing complex requirements. The SCR method offers a powerful family of analy…