G Giray - Journal of Systems and Software, 2021 - Elsevier
Context: Advancements in machine learning (ML) lead to a shift from the traditional view of software development, where algorithms are hard-coded by humans, to ML systems …
Tests that fail inconsistently, without changes to the code under test, are described as flaky. Flaky tests do not give a clear indication of the presence of software bugs and thus limit the …
Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …
We present Bugs. jar, a large-scale dataset for research in automated debugging, patching, and testing of Java programs. Bugs. jar is comprised of 1,158 bugs and patches, drawn from …
Continuous integration (CI) systems automate the compilation, building, and testing of software. Despite CI being a widely used activity in software engineering, we do not know …
We report our experience with SapFix: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code. We have used …
Developers often run tests to check that their latest changes to a code repository did not break any previously working functionality. Ideally, any new test failures would indicate …
S Wang, M Wen, B Lin, H Wu, Y Qin, D Zou… - Proceedings of the 35th …, 2020 - dl.acm.org
Test-based automated program repair (APR) has attracted huge attention from both industry and academia. Despite the significant progress made in recent studies, the overfitting …
Flaky tests are software tests that exhibit a seemingly random outcome (pass or fail) despite exercising unchanged code. In this work, we examine the perceptions of software …