Much research on Machine Learning testing relies on empirical studies th...
Large Language Models (LLMs) have shown promise in multiple software
eng...
Testing deep learning-based systems is crucial but challenging due to th...
The costly human effort required to prepare the training data of machine...
Asynchronous waits are one of the most prevalent root causes of flaky te...
The next era of program understanding is being propelled by the use of
m...
System goals are the statements that, in the context of software require...
With the increasing release of powerful language models trained on large...
Flaky tests are tests that pass and fail on different executions of the ...
While leveraging additional training data is well established to improve...
In this paper, we propose an assertion-based approach to capture softwar...
Specification inference techniques aim at (automatically) inferring a se...
Mutation testing is an established fault-based testing technique. It ope...
Mutation testing has been demonstrated to be one of the most powerful
fa...
Recently, deep neural networks (DNNs) have been widely applied in progra...
Graph neural networks (GNNs) have recently been popular in natural langu...
Flaky tests are tests that yield different outcomes when run on the same...
Much of software-engineering research relies on the naturalness of code,...
We propose transferability from Large Geometric Vicinity (LGV), a new
te...
Vulnerability prediction refers to the problem of identifying system
com...
Deep learning plays a more and more important role in our daily life due...
Flaky tests are defined as tests that manifest non-deterministic behavio...
Over the past few years, deep learning (DL) has been continuously expand...
In the last decade, researchers have studied fairness as a software prop...
Deep Neural Networks (DNNs) have gained considerable attention in the pa...
Various deep neural networks (DNNs) are developed and reported for their...
Many software engineering techniques, such as fault localization, operat...
We introduce μBERT, a mutation testing tool that uses a pre-trained
lang...
When software evolves, opportunities for introducing faults appear.
Ther...
Fault seeding is typically used in controlled studies to evaluate and co...
Mutation testing research has indicated that a major part of its applica...
Test flakiness forms a major testing concern. Flaky tests manifest
non-d...
Active learning is an established technique to reduce the labeling cost ...
Code embedding is a keystone in the application of machine learning on
s...
Flakiness is a major concern in Software testing. Flaky tests pass and f...
Test smells are known as bad development practices that reflect poor des...
Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...
The reactive synthesis problem consists of automatically producing
corre...
Background: Test flakiness is identified as a major issue that compromis...
Vulnerability prediction refers to the problem of identifying the system...
Semi-Supervised Learning (SSL) aims to maximize the benefits of learning...
Much research on software engineering and software testing relies on
exp...
Deep neural networks are vulnerable to evasion attacks, i.e., carefully
...
The rapid spread of the Coronavirus SARS-2 is a major challenge that led...
We introduce SeMu, a Dynamic Symbolic Execution technique that generates...
Much research on software testing makes an implicit assumption that test...
We propose adversarial embedding, a new steganography and watermarking
t...
This paper presents TransRepair, a fully automatic approach for testing ...
Testing of deep learning models is challenging due to the excessive numb...
Deep Neural Networks (DNNs) are intensively used to solve a wide variety...