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Article Citation - WoS: 16Citation - Scopus: 19Data Complexity Metrics for Xml Web Services(Univ Suceava, Fac Electrical Eng, 2009) Basci, Dilek; Misra, SanjayWeb services that are based on eXtensible Markup Language (XML) technologies enable integration of diverse IT processes and systems and have been gaining extraordinary acceptance from the basic to the most complicated business and scientific processes. The maintainability is one of the important factors that affect the quality of the Web services that can be seen a kind of software project. The effective management of any type of software projects requires modelling, measurement, and quantification. This study presents a metric for the assessment of the quality of the Web services in terms of its maintainability. For this purpose we proposed a data complexity metric that can be evaluated by analyzing WSDL (Web Service Description Language) documents used for describing Web services.Article Citation - WoS: 28Citation - Scopus: 28Experience in Predicting Fault-Prone Software Modules Using Complexity Metrics(Nctu-national Chiao Tung Univ Press, 2012) Yu, Liguo; Mishra, AlokComplexity metrics have been intensively studied in predicting fault-prone software modules. However, little work is done in studying how to effectively use the complexity metrics and the prediction models under realistic conditions. In this paper, we present a study showing how to utilize the prediction models generated from existing projects to improve the fault detection on other projects. The binary logistic regression method is used in studying publicly available data of five commercial products. Our study shows (1) models generated using more datasets can improve the prediction accuracy but not the recall rate; (2) lowering the cut-off value can improve the recall rate, but the number of false positives will be increased, which will result in higher maintenance effort. We further suggest that in order to improve model prediction efficiency, the selection of source datasets and the determination of cut-Off values should be based on specific properties of a project. So far, there are no general rules that have been found and reported to follow
