Software metrics fluctuation: a property for assisting the metric selection process

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• 作者：Elvira-Maria Arvanitou^a ; ^{e.m.arvanitou@rug.nl" class="auth_mail" title="E-mail the corresponding author} ; Apostolos Ampatzoglou^a ; ^{apostolos.ampatzoglou@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; ^{a.ampatzoglou@rug.nl" class="auth_mail" title="E-mail the corresponding author} ; Alexander Chatzigeorgiou^b ; ^{achat@uom.gr" class="auth_mail" title="E-mail the corresponding author} ; Paris Avgeriou^a ; ^{paris@cs.rug.nl" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Object-oriented metrics ; Fluctuation ; Case study ; Software evolution
• 刊名：Information and Software Technology
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：72
• 期：Complete
• 页码：110-124
• 全文大小：1190 K

文摘

Software quality attributes are assessed by employing appropriate metrics. However, the choice of such metrics is not always obvious and is further complicated by the multitude of available metrics. To assist metrics selection, several properties have been proposed. However, although metrics are often used to assess successive software versions, there is no property that assesses their ability to capture structural changes along evolution.

Objective

We introduce a property, Software Metric Fluctuation (SMF), which quantifies the degree to which a metric score varies, due to changes occurring between successive system's versions. Regarding SMF, metrics can be characterized as sensitive (changes induce high variation on the metric score) or stable (changes induce low variation on the metric score).

Method

SMF property has been evaluated by: (a) a case study on 20 OSS projects to assess the ability of SMF to differently characterize different metrics, and (b) a case study on 10 software engineers to assess SMF's usefulness in the metric selection process.

Results

The results of the first case study suggest that different metrics that quantify the same quality attributes present differences in their fluctuation. We also provide evidence that an additional factor that is related to metrics’ fluctuation is the function that is used for aggregating metric from the micro to the macro level. In addition, the outcome of the second case study suggested that SMF is capable of helping practitioners in metric selection, since: (a) different practitioners have different perception of metric fluctuation, and (b) this perception is less accurate than the systematic approach that SMF offers.

Conclusions

SMF is a useful metric property that can improve the accuracy of metrics selection. Based on SMF, we can differentiate metrics, based on their degree of fluctuation. Such results can provide input to researchers and practitioners in their metric selection processes.