TITLE:
Statistical Analysis of Process Monitoring Data for Software Process Improvement and Its Application
AUTHORS:
Kazuhiro Esaki, Yuki Ichinose, Shigeru Yamada
KEYWORDS:
Software Process Improvement; Process Monitoring; Design Quality Assessment; Multiple Regression Analysis; Principal Component Analysis; Factor Analysis; Quantitative Project Assessment
JOURNAL NAME:
American Journal of Operations Research,
Vol.2 No.1,
March
14,
2012
ABSTRACT: Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy for project managers to deal with these risks completely. Therefore, it is essential to manage the process quality by promoting activities of process monitoring and design quality assessment. In this paper, we discuss statistical data analysis for actual project management activities in process monitoring and design quality assessment, and analyze the effects for these software process improvement quantitatively by applying the methods of multivariate analysis. Then, we show how process factors affect the management measures of QCD (Quality, Cost, Delivery) by applying the multiple regression analyses to observed process monitoring data. Further, we quantitatively evaluate the effect by performing design quality assessment based on the principal component analysis and the factor analysis. As a result of analysis, we show that the design quality assessment activities are so effective for software process improvement. Further, based on the result of quantitative project assessment, we discuss the usefulness of process monitoring progress assessment by using a software reliability growth model. This result may enable us to give a useful quantitative measure of product release determination.