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09/11/2023
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
Much research on Machine Learning testing relies on empirical studies th...
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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...
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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...
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05/24/2023
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks
Natural Language Processing (NLP) models based on Machine Learning (ML) ...
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04/05/2023
Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability
Transferability is the property of adversarial examples to be misclassif...
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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...
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03/09/2023
ACoRe: Automated Goal-Conflict Resolution
System goals are the statements that, in the context of software require...
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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 ...
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02/06/2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
While leveraging additional training data is well established to improve...
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01/28/2023
Assertion Inferring Mutants
Specification inference techniques aim at (automatically) inferring a se...
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12/15/2022
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...
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10/06/2022
Enhancing Code Classification by Mixup-Based Data Augmentation
Recently, deep neural networks (DNNs) have been widely applied in progra...
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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...
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07/26/2022
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
We propose transferability from Large Geometric Vicinity (LGV), a new te...
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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...
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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...
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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...
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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...
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05/18/2022
Software Fairness: An Analysis and Survey
In the last decade, researchers have studied fairness as a software prop...
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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...
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04/08/2022
Labeling-Free Comparison Testing of Deep Learning Models
Various deep neural networks (DNNs) are developed and reported for their...
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02/07/2022
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks
While the literature on security attacks and defense of Machine Learning...
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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...
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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 ...
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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...
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12/02/2021
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space
The generation of feasible adversarial examples is necessary for properl...
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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...
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10/26/2021
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...
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09/27/2021
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, whic...
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05/26/2021
Automated Repair of Unrealisable LTL Specifications Guided by Model Counting
The reactive synthesis problem consists of automatically producing corre...
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04/15/2021
On the Use of Mutation in Injecting Test Order-Dependency
Background: Test flakiness is identified as a major issue that compromis...
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12/21/2020
Learning To Predict Vulnerabilities From Vulnerability-Fixes: A Machine Translation Approach
Vulnerability prediction refers to the problem of identifying the system...
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12/14/2020
Effective and Efficient Data Poisoning in Semi-Supervised Learning
Semi-Supervised Learning (SSL) aims to maximize the benefits of learning...
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12/11/2020
IBIR: Bug Report driven Fault Injection
Much research on software engineering and software testing relies on exp...
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11/10/2020
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
Deep neural networks are vulnerable to evasion attacks, i.e., carefully ...
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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...
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01/09/2020
Killing Stubborn Mutants with Symbolic Execution
We introduce SeMu, a Dynamic Symbolic Execution technique that generates...
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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...
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11/14/2019
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique
We propose adversarial embedding, a new steganography and watermarking t...
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04/30/2019
Test Selection for Deep Learning Systems
Testing of deep learning models is challenging due to the excessive numb...
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04/09/2019