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• W3116139030 endingPage "38" @default.
• W3116139030 startingPage "1" @default.
• W3116139030 abstract "Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location and severity of defects in code. Serious questions have been raised, however, over the quality of the data used in ESE. Data quality problems caused by noise, outliers, and incompleteness have been noted as being especially prevalent. Other quality issues, although also potentially important, have received less attention. In this study, we assess the quality of 13 datasets that have been used extensively in research on software effort estimation. The quality issues considered in this article draw on a taxonomy that we published previously based on a systematic mapping of data quality issues in ESE. Our contributions are as follows: (1) an evaluation of the fitness for purpose of these commonly used datasets and (2) an assessment of the utility of the taxonomy in terms of dataset benchmarking. We also propose a template that could be used to both improve the ESE data collection/submission process and to evaluate other such datasets, contributing to enhanced awareness of data quality issues in the ESE community and, in time, the availability and use of higher-quality datasets." @default.
• W3116139030 created "2021-01-05" @default.
• W3116139030 creator A5043958822 @default.
• W3116139030 creator A5044777354 @default.
• W3116139030 date "2019-09-23" @default.
• W3116139030 modified "2023-10-10" @default.
• W3116139030 title "Experience" @default.
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• W3116139030 doi "https://doi.org/10.1145/3328746" @default.
• W3116139030 hasPublicationYear "2019" @default.

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