CMS tool: calculating defect and change data from software project repositories

Defect and change prediction is a very important activity in software development. Predicting erroneous classes of the system early in the software development life cycle will enable early identification of risky classes in the initial phases. This will assist software practitioners in designing and developing software systems of better quality with focused resources and hence take necessary corrective design actions. In this work we describe a framework to develop and calculate the defect fixes and changes made during various versions of a software system. We develop a tool, Configuration Management System (CMS), which uses log files obtained from a Concurrent Versioning System (CVS) repository in order to collect the number of defects from each class. The tool also calculates the number of changes made during each version of the software. This tool will also assist software practitioners and researchers in collecting defect and change data for software systems.

[1]  A. Zeller,et al.  Predicting Defects for Eclipse , 2007, Third International Workshop on Predictor Models in Software Engineering (PROMISE'07: ICSE Workshops 2007).

[2]  Saeed Parsa,et al.  A Learning Approach to Early Bug Prediction in Deployed Software , 2008, AIMSA.

[3]  W. Deming Out of the crisis : quality, productivity and competitive position , 1986 .

[4]  Ruchika Malhotra,et al.  Investigation of relationship between object-oriented metrics and change proneness , 2013, Int. J. Mach. Learn. Cybern..

[5]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[6]  Yuming Zhou,et al.  Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness , 2009, IEEE Transactions on Software Engineering.

[7]  Liang Ping,et al.  A Change-Oriented Conceptual Framework Of Software Configuration Management , 2007, 2007 International Conference on Service Systems and Service Management.

[8]  Rob Pooley,et al.  Collecting and Analyzing Web-Based Project Metrics , 2002, IEEE Softw..

[9]  Tao Xing Software configuration management of change control study based on baseline , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[10]  Naheed Azeem,et al.  Defect Prediction Leads to High Quality Product , 2011 .

[11]  Sergiu M. Dascalu,et al.  DuoTracker: Tool Support for Software Defect Data Collection and Analysis , 2006, 2006 International Conference on Software Engineering Advances (ICSEA'06).

[12]  Harvey P. Siy,et al.  Predicting Fault Incidence Using Software Change History , 2000, IEEE Trans. Software Eng..

[13]  Jürgen Schönwälder,et al.  Applying Semantic Techniques to Search and Analyze Bug Tracking Data , 2009, Journal of Network and Systems Management.

[14]  Haruhiko Kaiya,et al.  Adapting a fault prediction model to allow inter languagereuse , 2008, PROMISE '08.