Dynamic Test Case Prioritization in Industrial Test Result Datasets
Publiceringsår
2024
Upphovspersoner
Alina Torbunova; Per Erik Strandberg; Ivan Porres
Abstrakt
Regression testing in software development checks if new software features affect existing ones. Regression testing is a key task in<br/>continuous development and integration, where software is built in small increments and new features are integrated as soon as<br/>possible. It is therefore important that developers are notified about possible faults quickly. In this article, we propose a test case prioritization schema that combines the use of a static and a dynamic prioritization algorithm. The dynamic prioritization algorithm rearranges the order of execution of tests on the fly, while the tests are being executed. We propose to use a conditional probability<br/>dynamic algorithm for this. We evaluate our solution on three industrial datasets and utilize Average Percentage of Fault Detection<br/>for that. The main findings are that our dynamic prioritization algorithm can: a) be applied with any static algorithm that assigns<br/>a priority score to each test case b) can improve the performance of the static algorithm if there are failure correlations between test<br/>cases c) can also reduce the performance of the static algorithm, but only when the static scheduling is performed at a near optimal<br/>level.<br/>
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Publikationstyp
Publikationsform
Artikel
Moderpublikationens typ
Konferens
Artikelstyp
Annan artikel
Målgrupp
VetenskapligKollegialt utvärderad
Kollegialt utvärderadUKM:s publikationstyp
A4 Artikel i en konferenspublikationPublikationskanalens uppgifter
Journal/Serie
Proceedings - 2024 IEEE/ACM International Conference on Automation of Software Test, AST 2024
Moderpublikationens namn
Proceedings - 2024 IEEE/ACM International Conference on Automation of Software Test, AST 2024
Sidor
154-158
ISBN
Publikationsforum
Publikationsforumsnivå
1
Öppen tillgång
Öppen tillgänglighet i förläggarens tjänst
Ja
Öppen tillgång till publikationskanalen
Delvis öppen publikationskanal
Parallellsparad
Ja
Övriga uppgifter
Vetenskapsområden
Data- och informationsvetenskap
Nyckelord
[object Object],[object Object],[object Object]
Förlagets internationalitet
Internationell
Språk
engelska
Internationell sampublikation
Ja
Sampublikation med ett företag
Ja
DOI
10.1145/3644032.3644452
Publikationen ingår i undervisnings- och kulturministeriets datainsamling
Ja