Software quality metrics aggregation in industry. Issue 10 (13th August 2012)

• Record Type: Journal Article
• Authors: Mordal, Karine; Anquetil, Nicolas; Laval, Jannik; Serebrenik, Alexander; Vasilescu, Bogdan; Ducasse, Stéphane
• Other Names: Mens Tom guestEditor.
• Is Part Of: Journal of software. Volume 25:Issue 10(2013)
• Page Start: 1117
• Page End: 1135
• Publication Date: 2012-08-13
• Subjects: software metrics -- software quality -- aggregation -- inequality indices; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1558
• Languages: English
• ISSNs: 2047-7473

Abstract

SUMMARY: With the growing need for quality assessment of entire software systems in the industry, new issues are emerging. First, because most software quality metrics are defined at the level of individual software components, there is a need for aggregation methods to summarize the results at the system level. Second, because a software evaluation requires the use of different metrics, with possibly widely varying output ranges, there is a need to combine these results into a unified quality assessment. In this paper we derive, from our experience on real industrial cases and from the scientific literature, requirements for an aggregation method. We then present a solution through the Squale model for metric aggregation, a model specifically designed to address the needs of practitioners. We empirically validate the adequacy of Squale through experiments onEclipse . Additionally, we compare the Squale model to both traditional aggregation techniques (e.g., the arithmetic mean), and to econometric inequality indices (e.g., the Gini or the Theil indices), recently applied to aggregation of software metrics. Copyright © 2012 John Wiley & Sons, Ltd. Abstract : Quality assessement of entire software systems raises issues due to the necessity to combine and aggregate different metrics at different levels. We describe requirements for an aggregation method and present a solution for a successfull metric aggregation. Then we compare it with traditional methods and econometric inequality indices.