Explainable Priority Assessment of Software-Defects using Categorical Features at SAP HANA [Elektronisk resurs]
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Lenz, Luca (författare)
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24th Evaluation and Assessment in Software Engineering Conference, EASE 2020, Online, Norway, 15 April 2020 through 17 April 2020
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Felderer, Michael, 1978- (författare)
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Schwedes, Sascha (författare)
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Müller, Kai (författare)
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Blekinge Tekniska Högskola Fakulteten för datavetenskaper (utgivare)
- Publicerad: Association for Computing Machinery, 2020
- Engelska.
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Ingår i: ACM International Conference Proceeding Series. ; 366-367
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- Relaterad länk:
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http://www.bth.se/ (Värdpublikation)
Sammanfattning
Ämnesord
Stäng
- We want to automate priority assessment of software defects. To do so we provide a tool which uses an explainability-driven framework and classical machine learning algorithms to keep the decisions transparent. Differing from other approaches we only use objective and categorical fields from the bug tracking system as features. This makes our approach lightweight and extremely fast. We perform binary classification with priority labels corresponding to deadlines. Additionally, we evaluate the tool on real data to ensure good performance in the practical use case. © 2020 ACM.
Ämnesord
- Natural Sciences (hsv)
- Computer and Information Sciences (hsv)
- Computer Sciences (hsv)
- Naturvetenskap (hsv)
- Data- och informationsvetenskap (hsv)
- Datavetenskap (datalogi) (hsv)
Genre
- government publication (marcgt)
Indexterm och SAB-rubrik
- bug priority
- defect assessment
- machine learning
- software quality
- Defects
- Learning algorithms
- Binary classification
- Bug tracking system
- Categorical features
- Practical use
- Priority assessment
- Software defects
- Software engineering
Inställningar
Hjälp
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