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Improving Code Smel...
Improving Code Smell Predictions in Continuous Integration by Differentiating Organic from Cumulative Measures
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- Al Mamun, Md Abdullah, 1982 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Staron, Miroslaw, 1977 (författare)
- Göteborgs universitet,University of Gothenburg
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- Berger, Christian, 1980 (författare)
- Göteborgs universitet,University of Gothenburg
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visa fler...
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- Hebig, Regina, 1984 (författare)
- Göteborgs universitet,University of Gothenburg
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- Hansson, Jörgen, 1970 (författare)
- Högskolan i Skövde,University of Skövde
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visa färre...
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(creator_code:org_t)
- 2019
- 2019
- Engelska.
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Ingår i: The Fifth International Conference on Advances and Trends in Software Engineering. - 2519-8394. - 9781510883741 ; , s. 62-71
- Relaterad länk:
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https://research.cha...
Abstract
Ämnesord
Stäng
- Continuous integration and deployment are enablers of quick innovation cycles of software and systems through incremental releases of a product within short periods of time. If software qualities can be predicted for the next release, quality managers can plan ahead with resource allocation for concerning issues. Cumulative metrics are observed to have much higher correlation coefficients compared to non-cumulative metrics. Given the difference in correlation coefficients of cumulative and noncumulative metrics, this study investigates the difference between metrics of these two categories concerning the correctness of predicting code smell which is internal software quality. This study considers 12 metrics from each measurement category, and 35 code smells collected from 36,217 software revisions (commits) of 242 open source Java projects. We build 8,190 predictive models and evaluate them to determine how measurement categories of predictors and targets affect model accuracies predicting code smells. To further validate our approach, we compared our results with Principal Component Analysis (PCA), a statistical procedure for dimensionality reduction. Results of the study show that within the context of continuous integration, non-cumulative metrics as predictors build better predictive models with respect to model accuracy compared to cumulative metrics. When the results are compared with models built from extracted PCA components, we found better results using our approach.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- organic metrics
- time-series cross-validation
- principal component analysis
- Software metrics
- code smells
- training-test-split cross-validation
- cumulative metrics
- random forest
- effects of measurement types
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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