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Fabian Scharnböck

dblp:358/6931 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0007-1823-6985ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 87% Empirical software engineering · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
flaky test
0.812024
Do Automatic Test Generation Tools Generate Flaky Tests? · ICSE 2024
Software testing
test generation
0.812024
Do Automatic Test Generation Tools Generate Flaky Tests? · ICSE 2024
Empirical software engineering
mining software repositories
0.212024
Do Automatic Test Generation Tools Generate Flaky Tests? · ICSE 2024

Methods — techniques the papers use, named apart from their topics

statistical analysis · 0.8differential execution · 0.8
YearPublicationVenuePosition
2024 Do Automatic Test Generation Tools Generate Flaky Tests?
abstract
Non-deterministic test behavior, or flakiness, is common and dreaded among developers. Researchers have studied the issue and proposed approaches to mitigate it. However, the vast majority of previous work has only considered developer-written tests. The prevalence and nature of flaky tests produced by test generation tools remain largely unknown. We ask whether such tools also produce flaky tests and how these differ from developer-written ones. Furthermore, we evaluate mechanisms that suppress flaky test generation. We sample 6 356 projects written in Java or Python. For each project, we generate tests using EvoSuite (Java) and Pynguin (Python), and execute each test 200 times, looking for inconsistent outcomes. Our results show that flakiness is at least as common in generated tests as in developer-written tests. Nevertheless, existing flakiness suppression mechanisms implemented in EvoSuite are effective in alleviating this issue (71.7 % fewer flaky tests). Compared to developer-written flaky tests, the causes of generated flaky tests are distributed differently. Their non-deterministic behavior is more frequently caused by randomness, rather than by networking and concurrency. Using flakiness suppression, the remaining flaky tests differ significantly from any flakiness previously reported, where most are attributable to runtime optimizations and EvoSuite-internal resource thresholds. These insights, with the accompanying dataset, can help maintainers to improve test generation tools, give recommendations for developers using these tools, and serve as a foundation for future research in test flakiness or test generation.
Martin Gruber, Muhammad Firhard Roslan, Owain Parry, Fabian Scharnböck, Phil McMinn, Gordon Fraser 0001
ICSE4
2024 MQfilTTr: Strengthening Smart Home Privacy Through MQTT Traffic Manipulation
Henrich Christopher Pöhls, Sven Gebauer, Fabian Scharnböck, Korbinian Spielvogel, Joachim Posegga
WISTP3