Niclas Alexandersson

dblp:345/0635 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-4308-4040ORCID · reported

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

Software 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 · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
mutation testing
0.712023
Automated Generation and Evaluation of JMH Microbenchmark Suites From Unit Tests · IEEE Trans. Software Eng. 2023
Software testing
performance testing
0.712023
Automated Generation and Evaluation of JMH Microbenchmark Suites From Unit Tests · IEEE Trans. Software Eng. 2023

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

mutation testing · 0.7
YearPublicationVenuePosition
2023 Automated Generation and Evaluation of JMH Microbenchmark Suites From Unit Tests
abstract
Performance is a crucial non-functional requirement of many software systems. Despite the widespread use of performance testing, developers still struggle to construct and evaluate the quality of performance tests. To address these two major challenges, we implement a framework, dubbedju2jmh, to automatically generate performance microbenchmarks from JUnit tests and use mutation testing to study the quality of generated microbenchmarks. Specifically, we compare ourju2jmhgenerated benchmarks to manually written JMH benchmarks and to automatically generated JMH benchmarks using the AutoJMH framework, as well as directly measuring system performance with JUnit tests. For this purpose, we have conducted a study on three subjects (Rxjava,Eclipse-collections, andZipkin) with$\sim$454Ksource lines of code(SLOC), 2,417 JMH benchmarks (including manually written and generated AutoJMH benchmarks) and 35,084 JUnit tests. Our results show that theju2jmhgenerated JMH benchmarks consistently outperform using the execution time and throughput of JUnit tests as a proxy of performance and JMH benchmarks automatically generated using the AutoJMH framework while being comparable to JMH benchmarks manually written by developers in terms of tests’ stability and ability to detect performance bugs. Nevertheless,ju2jmhbenchmarks are able to cover more of the software applications than manually written JMH benchmarks during the microbenchmark execution. Furthermore,ju2jmhbenchmarks are generated automatically, while manually written JMH benchmarks require many hours of hard work and attention; therefore our study can reduce developers’ effort to construct microbenchmarks. In addition, we identify three factors (too low test workload, unstable tests and limited mutant coverage) that affect a benchmark's ability to detect performance bugs. To the best of our knowledge, this is the first study aimed at assisting developers in fully automated microbenchmark creation and assessing microbenchmark quality for performance testing.
Mostafa Jangali, Yiming Tang 0002, Niclas Alexandersson, Philipp Leitner 0001, Jinqiu Yang 0001, Weiyi Shang
IEEE Trans. Software Eng.3