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Explainable Priorit...
Explainable Priority Assessment of Software-Defects using Categorical Features at SAP HANA
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- Lenz, Luca (author)
- Sap
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- Felderer, Michael, 1978- (author)
- Blekinge Tekniska Högskola,Institutionen för programvaruteknik,University of Innsbruck, AUT
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- Schwedes, Sascha (author)
- Sap
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- Müller, Kai (author)
- Sap
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(creator_code:org_t)
- 2020-04-17
- 2020
- English.
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In: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery. - 9781450377317 ; , s. 366-367
- Related links:
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https://bth.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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