Experience: Quality benchmarking of datasets used in software effort estimation

Bosu, Michael Franklin and MacDonell, Stephen G. (2019) Experience: Quality benchmarking of datasets used in software effort estimation. Journal of Data and Information Quality, 11 (4). pp. 1-38. ISSN 2524-6364

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Official URL: https://jdiq.acm.org/

Abstract or Summary

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.

Item Type:Journal article
Keywords that describe the item:Data quality, benchmarking, empirical software engineering, software effort estimation, noise, missing data
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
ID Code:6910
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Deposited On:17 Sep 2019 03:11
Last Modified:17 Sep 2019 03:11

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