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Author: Xiaoxing Yang

Affiliation: School of Data and Computer Science, Sun Yat-Sen University, Guangzhou and China

Keyword(s): Software Defect Prediction, Sorting Modules in Order of Defect Count, Software Metrics, Correlation Analysis, Metric Analysis.

Abstract: Sorting software modules in order of defect count can help testers to focus on software modules with more defects. Many approaches have been proposed to accomplish this. In order to compare approaches more fairly, researchers have provided publicly available data sets. In this paper, we provide a new metric selection approach and evaluate the usefulness of software metrics of eleven publicly available data sets, in order to investigate the quality of these data sets and find out the software metrics that are most efficient for sorting modules in order of defect count. Unexpectedly, experimental results show that only one metric can work well over most of these data sets, which implies that more effective metrics should be introduced. We also obtain other findings from these data sets, which can help to introduce new metrics for sorting software modules in order of defect count to some extent.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Yang and X. (2019). Evaluating Software Metrics for Sorting Software Modules in Order of Defect Count. In Proceedings of the 14th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-379-7; ISSN 2184-2833, SciTePress, pages 94-105. DOI: 10.5220/0007924800940105

@conference{icsoft19,
author={Xiaoxing Yang},
title={Evaluating Software Metrics for Sorting Software Modules in Order of Defect Count},
booktitle={Proceedings of the 14th International Conference on Software Technologies - ICSOFT},
year={2019},
pages={94-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007924800940105},
isbn={978-989-758-379-7},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Software Technologies - ICSOFT
TI - Evaluating Software Metrics for Sorting Software Modules in Order of Defect Count
SN - 978-989-758-379-7
IS - 2184-2833
AU - Yang, X.
PY - 2019
SP - 94
EP - 105
DO - 10.5220/0007924800940105
PB - SciTePress

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