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|Title:||A multivariate statistical framework for the analysis of software effort phase distribution||Authors:||Chatzipetrou, Panagiota
Andreou, Andreas S.
|Keywords:||Biplot;Cluster analysis;Compositional data analysis;Phased effort analysis;Software effort distribution||Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||1-Jan-2015||Publisher:||Elsevier||Source:||Information and Software Technology, 2015, Volume 59, Pages 149-169||metadata.dc.doi:||http://dx.doi.org/10.1016/j.infsof.2014.11.004||Abstract:||Context In software project management, the distribution of resources to various project activities is one of the most challenging problems since it affects team productivity, product quality and project constraints related to budget and scheduling. Objective The study aims to (a) reveal the high complexity of modelling the effort usage proportion in different phases as well as the divergence from various rules-of-thumb in related literature, and (b) present a systematic data analysis framework, able to offer better interpretations and visualisation of the effort distributed in specific phases. Method The basis for the proposed multivariate statistical framework is Compositional Data Analysis, a methodology appropriate for proportions, along with other methods like the deviation from rules-of-thumb, the cluster analysis and the analysis of variance. The effort allocations to phases, as reported in around 1500 software projects of the ISBSG R11 repository, were transformed to vectors of proportions of the total effort and were analysed with respect to prime project attributes. Results The proposed statistical framework was able to detect high dispersion among data, distribution inequality and various interesting correlations and trends, groupings and outliers, especially with respect to other categorical and continuous project attributes. Only a very small number of projects were found close to the rules-of-thumb from the related literature. Significant differences in the proportion of effort spent in different phrases for different types of projects were found. Conclusion There is no simple model for the effort allocated to phases of software projects. The data from previous projects can provide valuable information regarding the distribution of the effort for various types of projects, through analysis with multivariate statistical methodologies. The proposed statistical framework is generic and can be easily applied in a similar sense to any dataset containing effort allocation to phases.||URI:||http://ktisis.cut.ac.cy/handle/10488/9494||ISSN:||09505849||Rights:||© 2014 Elsevier B.V.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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