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Improving Data Qual...
Improving Data Quality for Regression Test Selection by Reducing Annotation Noise
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- Al Sabbagh, Khaled, 1987 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, datavetenskap (GU),Department of Computer Science and Engineering, Computing Science (GU)
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- Staron, Miroslaw, 1977 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, Software Engineering (GU),Software Center,Institutionen för data- och informationsteknik (GU),Institutionen för data- och informationsteknik, Software Engineering (GU),Department of Computer Science and Engineering (GU)
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- Hebig, Regina, 1984 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Institutionen för data- och informationsteknik, Software Engineering (GU),Department of Computer Science and Engineering (GU),Institutionen för data- och informationsteknik, Software Engineering (GU)
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- Meding, Wilhelm, 1970 (författare)
- Telefonaktiebolaget L M Ericsson,Ericsson
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(creator_code:org_t)
- 2020
- 2020
- Engelska.
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Ingår i: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. ; , s. 191-194
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https://research.cha...
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https://doi.org/10.1...
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https://gup.ub.gu.se...
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Abstract
Ämnesord
Stäng
- Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the identification and reduction of noise that often comes in large data. In this paper, we present a noise reduction approach that deals with the problem of contradictory training entries. We empirically evaluate the effectiveness of the approach in the context of selective regression testing. For this purpose, we use a curated training set as input to a tree-based machine learning ensemble and compare the classification precision, recall, and f-score against a non-curated set. Our study shows that using the noise reduction approach on the training instances gives better results in prediction with an improvement of 37% on precision, 70% on recall, and 59% on f-score.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Nyckelord
- Machine Learning Models
- Regression Testing
- Annotation Noise
- Annotation Noise
- Machine Learning Models
- Regression Testing
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)