In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90 of all cases.
@cite_35 @cite_19 point out the need for aggregating metrics as most of them are defined on a micro level, such as functions or classes. However, conclusions have be drawn on a component or system level. @cite_14 point out the deficiencies of using the average to aggregate metric scores. They introduce the model, which allows effectively comparing different metric values by normalising them into a given interval of values.
@cite_19 @cite_35 @cite_14
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Fault prediction models usually employ software metrics which were previously shown to be a strong predictor for defects, e.g., SLOC. However, metrics are usually defined on a microlevel (method, class, package), and should therefore be aggregated in order to provide insights in the evolution at the macro-level (system). In addition to traditional aggregation techniques such as the mean, median, or sum, recently econometric aggregation techniques, such as the Gini, Theil, and Hoover indices have been proposed. In this paper we wish to understand whether the aggregation technique influences the presence and strength of the relation between SLOC and defects. Our results indicate that correlation is not strong, and is influenced by the aggregation technique. 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 on Eclipse. 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. Keywords: software metrics; software quality; aggregation; inequality indices