Mike Papadakis

research

∙ 09/11/2023

Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations

Much research on Machine Learning testing relies on empirical studies th...

0 Salah Ghamizi, et al. ∙

research

∙ 08/29/2023

Large Language Models in Fault Localisation

Large Language Models (LLMs) have shown promise in multiple software eng...

0 Yonghao Wu, et al. ∙

research

∙ 07/29/2023

Evaluating the Robustness of Test Selection Methods for Deep Neural Networks

Testing deep learning-based systems is crucial but challenging due to th...

0 Qiang Hu, et al. ∙

research

∙ 06/02/2023

Active Code Learning: Benchmarking Sample-Efficient Training of Code Models

The costly human effort required to prepare the training data of machine...

0 Qiang Hu, et al. ∙

research

∙ 05/15/2023

Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing

Asynchronous waits are one of the most prevalent root causes of flaky te...

0 Yu Pei, et al. ∙

research

∙ 03/13/2023

Boosting Source Code Learning with Data Augmentation: An Empirical Study

The next era of program understanding is being propelled by the use of m...

0 Zeming Dong, et al. ∙

research

∙ 03/09/2023

ACoRe: Automated Goal-Conflict Resolution

System goals are the statements that, in the context of software require...

0 Luiz Carvalho, et al. ∙

research

∙ 03/07/2023

Vulnerability Mimicking Mutants

With the increasing release of powerful language models trained on large...

0 Aayush Garg, et al. ∙

research

∙ 02/21/2023

The Importance of Discerning Flaky from Fault-triggering Test Failures: A Case Study on the Chromium CI

Flaky tests are tests that pass and fail on different executions of the ...

0 Guillaume Haben, et al. ∙

research

∙ 02/06/2023

GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

While leveraging additional training data is well established to improve...

1 Salah Ghamizi, et al. ∙

research

∙ 01/29/2023

Specification Inference for Evolving Systems

In this paper, we propose an assertion-based approach to capture softwar...

0 Renzo Degiovanni, et al. ∙

research

∙ 01/28/2023

Assertion Inferring Mutants

Specification inference techniques aim at (automatically) inferring a se...

0 Aayush Garg, et al. ∙

research

∙ 01/09/2023

Efficient Mutation Testing via Pre-Trained Language Models

Mutation testing is an established fault-based testing technique. It ope...

0 Ahmed Khanfir, et al. ∙

research

∙ 12/22/2022

Keeping Mutation Test Suites Consistent and Relevant with Long-Standing Mutants

Mutation testing has been demonstrated to be one of the most powerful fa...

0 Milos Ojdanic, et al. ∙

research

∙ 10/06/2022

Enhancing Code Classification by Mixup-Based Data Augmentation

Recently, deep neural networks (DNNs) have been widely applied in progra...

0 Zeming Dong, et al. ∙

research

∙ 10/06/2022

Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling

Graph neural networks (GNNs) have recently been popular in natural langu...

0 Zeming Dong, et al. ∙

research

∙ 08/31/2022

Predicting Flaky Tests Categories using Few-Shot Learning

Flaky tests are tests that yield different outcomes when run on the same...

0 Amal Akli, et al. ∙

research

∙ 08/11/2022

CodeBERT-nt: code naturalness via CodeBERT

Much of software-engineering research relies on the naturalness of code,...

0 Ahmed Khanfir, et al. ∙

research

∙ 07/26/2022

LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

We propose transferability from Large Geometric Vicinity (LGV), a new te...

0 Martin Gubri, et al. ∙

research

∙ 07/22/2022

Learning from what we know: How to perform vulnerability prediction using noisy historical data

Vulnerability prediction refers to the problem of identifying system com...

0 Aayush Garg, et al. ∙

research

∙ 07/22/2022

Efficient Testing of Deep Neural Networks via Decision Boundary Analysis

Deep learning plays a more and more important role in our daily life due...

9 Qiang Hu, et al. ∙

research

∙ 07/20/2022

What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness

Flaky tests are defined as tests that manifest non-deterministic behavio...

0 Sarra Habchi, et al. ∙

research

∙ 06/11/2022

CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning

Over the past few years, deep learning (DL) has been continuously expand...

4 Qiang Hu, et al. ∙

research

∙ 05/18/2022

Software Fairness: An Analysis and Survey

In the last decade, researchers have studied fairness as a software prop...

0 Ezekiel Soremekun, et al. ∙

research

∙ 04/08/2022

Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment

Deep Neural Networks (DNNs) have gained considerable attention in the pa...

2 Qiang Hu, et al. ∙

research

∙ 04/08/2022

Labeling-Free Comparison Testing of Deep Learning Models

Various deep neural networks (DNNs) are developed and reported for their...

0 Yuejun Guo, et al. ∙

research

∙ 03/21/2022

Using Evolutionary Coupling to Establish Relevance Links Between Tests and Code Units. A case study on fault localization

Many software engineering techniques, such as fault localization, operat...

0 Jeongju Sohn, et al. ∙

research

∙ 03/07/2022

μBERT: Mutation Testing using Pre-Trained Language Models

We introduce μBERT, a mutation testing tool that uses a pre-trained lang...

0 Renzo Degiovanni, et al. ∙

research

∙ 12/29/2021

Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution

When software evolves, opportunities for introducing faults appear. Ther...

0 Milos Ojdanic, et al. ∙

research

∙ 12/29/2021

Syntactic Vs. Semantic similarity of Artificial and Real Faults in Mutation Testing Studies

Fault seeding is typically used in controlled studies to evaluate and co...

0 Milos Ojdanic, et al. ∙

research

∙ 12/28/2021

Cerebro: Static Subsuming Mutant Selection

Mutation testing research has indicated that a major part of its applica...

0 Aayush Garg, et al. ∙

research

∙ 12/09/2021

A Qualitative Study on the Sources, Impacts, and Mitigation Strategies of Flaky Tests

Test flakiness forms a major testing concern. Flaky tests manifest non-d...

0 Sarra Habchi, et al. ∙

research

∙ 12/05/2021

Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models

Active learning is an established technique to reduce the labeling cost ...

0 Yuejun Guo, et al. ∙

research

∙ 12/02/2021

GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses

Code embedding is a keystone in the application of machine learning on s...

0 Wei Ma, et al. ∙

research

∙ 11/05/2021

Discerning Legitimate Failures From False Alerts: A Study of Chromium's Continuous Integration

Flakiness is a major concern in Software testing. Flaky tests pass and f...

0 Guillaume Haben, et al. ∙

research

∙ 11/03/2021

Smells in System User Interactive Tests

Test smells are known as bad development practices that reflect poor des...

0 Renaud Rwemalika, et al. ∙

research

∙ 10/26/2021

Adversarial Robustness in Multi-Task Learning: Promises and Illusions

Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...

0 Salah Ghamizi, et al. ∙

research

∙ 05/26/2021

Automated Repair of Unrealisable LTL Specifications Guided by Model Counting

The reactive synthesis problem consists of automatically producing corre...

0 Matías Brizzio, et al. ∙

research

∙ 04/15/2021

On the Use of Mutation in Injecting Test Order-Dependency

Background: Test flakiness is identified as a major issue that compromis...

0 Sarra Habchi, et al. ∙

research

∙ 12/21/2020

Learning To Predict Vulnerabilities From Vulnerability-Fixes: A Machine Translation Approach

Vulnerability prediction refers to the problem of identifying the system...

0 Aayush Garg, et al. ∙

research

∙ 12/14/2020

Effective and Efficient Data Poisoning in Semi-Supervised Learning

Semi-Supervised Learning (SSL) aims to maximize the benefits of learning...

0 Adriano Franci, et al. ∙

research

∙ 12/11/2020

IBIR: Bug Report driven Fault Injection

Much research on software engineering and software testing relies on exp...

0 Ahmed Khanfir, et al. ∙

research

∙ 11/10/2020

Efficient and Transferable Adversarial Examples from Bayesian Neural Networks

Deep neural networks are vulnerable to evasion attacks, i.e., carefully ...

0 Martin Gubri, et al. ∙

research

∙ 06/12/2020

Data-driven Simulation and Optimization for Covid-19 Exit Strategies

The rapid spread of the Coronavirus SARS-2 is a major challenge that led...

0 Salah Ghamizi, et al. ∙

research

∙ 01/09/2020

Killing Stubborn Mutants with Symbolic Execution

We introduce SeMu, a Dynamic Symbolic Execution technique that generates...

0 Thierry Titcheu Chekam, et al. ∙

research

∙ 12/06/2019

FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. A Case Study on Mutation Testing and Program Repair

Much research on software testing makes an implicit assumption that test...

0 Maxime Cordy, et al. ∙

research

∙ 11/14/2019

Adversarial Embedding: A robust and elusive Steganography and Watermarking technique

We propose adversarial embedding, a new steganography and watermarking t...

0 Salah Ghamizi, et al. ∙

research

∙ 10/07/2019

Automatic Testing and Improvement of Machine Translation

This paper presents TransRepair, a fully automatic approach for testing ...

0 Zeyu Sun, et al. ∙

research

∙ 04/30/2019

Test Selection for Deep Learning Systems

Testing of deep learning models is challenging due to the excessive numb...

0 Wei Ma, et al. ∙

research

∙ 04/09/2019

Automated Search for Configurations of Deep Neural Network Architectures

Deep Neural Networks (DNNs) are intensively used to solve a wide variety...

0 Salah Ghamizi, et al. ∙

Mike Papadakis

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