Assessing and Understanding Efficiency and Success of Software Production Export

@article{vonMayrhauser2000, abstract = {One of the goals of collectingproject data during software development and evolution is toassess how well the project did and what should be done to improvein the future. With the wide range of data often collected andthe many complicated relationships between them, this is notalways easy. This paper suggests to use production models (DataEnvelope Analysis) to analyze objective variables and their impacton efficiency. To understand the effect of subjective variables,it is suggested to apply principal component analysis (PCA).Further, we propose to combine the results from the productionmodels and the analysis of the subjective variables. We showcapabilities of production models and illustrate how productionmodels can be combined with other approaches to allow for assessingand hence understanding software project data. The approach isillustrated on a data set consisting of 46 software projectsfrom the NASA-SEL database (NASA-SEL, 1992). The data analyzedis of the type that is commonly found in project databases.}, author = {Mayrhauser, A. Von and Wohlin, C. and Ohlsson, M. C.}, citeulike-article-id = {6081605}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1009815306795}, citeulike-linkout-1 = {http://www.springerlink.com/content/t200455j04452762}, day = {1}, doi = {10.1023/A:1009815306795}, journal = {Empirical Software Engineering}, keywords = {efficiency, programming, software}, month = {June}, number = {2}, pages = {125--154}, posted-at = {2009-11-06 22:00:31}, priority = {2}, title = {Assessing and Understanding Efficiency and Success of Software Production}, url = {http://dx.doi.org/10.1023/A:1009815306795}, volume = {5}, year = {2000} }

TY - JOUR ID - vonMayrhauser2000 L3 - citeulike-article-id:6081605 N2 - One of the goals of collectingproject data during software development and evolution is toassess how well the project did and what should be done to improvein the future. With the wide range of data often collected andthe many complicated relationships between them, this is notalways easy. This paper suggests to use production models (DataEnvelope Analysis) to analyze objective variables and their impacton efficiency. To understand the effect of subjective variables,it is suggested to apply principal component analysis (PCA).Further, we propose to combine the results from the productionmodels and the analysis of the subjective variables. We showcapabilities of production models and illustrate how productionmodels can be combined with other approaches to allow for assessingand hence understanding software project data. The approach isillustrated on a data set consisting of 46 software projectsfrom the NASA-SEL database (NASA-SEL, 1992). The data analyzedis of the type that is commonly found in project databases. IS - 2 JF - Empirical Software Engineering EP - 154 TI - Assessing and Understanding Efficiency and Success of Software Production VL - 5 SP - 125 KW - efficiency KW - programming KW - software AU - Mayrhauser, Von AU - Wohlin, C AU - Ohlsson, MC PY - 2000/06/01/ UR - http://dx.doi.org/10.1023/A:1009815306795 ER -