An application of data envelopment analysis to software quality assessment

TitleAn application of data envelopment analysis to software quality assessment
Publication TypeConference Paper
Year of Publication2013
AuthorsPaschalidou, G, Stiakakis, E, Chatzigeorgiou, A
Secondary TitleProceedings of the 6th Balkan Conference in Informatics
Place PublishedNew York, NY, USA
ISBN Number978-1-4503-1851-8
Keywordsdea, design metrics, software evolution, software quality

Data Envelopment Analysis (DEA) is a non-parametric technique which involves the use of linear programming methods to measure the efficiency of a homogenous set of units. These units are known as Decision Making Units (DMUs) and defined by multiple input and output data. Efficiencies are measured relative to a piece-wise surface (efficient frontier) which envelops the data, thus justifying the name of the technique. Although DEA has been mostly used in production economics, its application in the context of software quality evaluation seems to be a promising approach. This study provides an application of DEA to assess the evolution of two open-source software projects in terms of selected metric values for successive versions of each project. What is really interesting in DEA is that a single efficiency score is calculated for each version despite the often convoluted overall picture of the metric values. According to a simplified view of DEA, there are two categories of units, the efficient (onto the efficient frontier) and the inefficient ones. Each inefficient unit is characterized by a reference set of peers which involves all the efficient units "operating" closer to that unit. Through the consideration of the reference set of the inefficient versions of each project, the metrics that require improvement, as well as the extent of improvement, could be estimated. These results could assist software developers in identifying design issues that require further improvement. Notwithstanding the fact that there are a number of issues to be further investigated, the applicability of DEA and other operations research tools in the context of software quality might yield interesting results.

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