Measuring fine-grained change in software: towards modification-aware change metrics

In this paper we propose the notion of change metrics, those that measure change in a project or its entities. In particular we are interested in measuring fine-grained changes, such as those stored by version control systems (such as CVS). A framework for the classification of change metrics is provided. We discuss the idea of change metrics which are modification aware, that is metrics which evaluate the change itself and not just the change in a measurement of the system before and after the change. We then provide examples of the use of these metrics on two mature projects

[1]  Dewayne E. Perry,et al.  Metrics and laws of software evolution-the nineties view , 1997, Proceedings Fourth International Software Metrics Symposium.

[2]  Michael W. Godfrey,et al.  Evolution in open source software: a case study , 2000, Proceedings 2000 International Conference on Software Maintenance.

[3]  Daniel M. Germán,et al.  Using software trails to reconstruct the evolution of software , 2004, J. Softw. Maintenance Res. Pract..

[4]  Sandeep Purao,et al.  Product metrics for object-oriented systems , 2003, CSUR.

[5]  Audris Mockus,et al.  Using Version Control Data to Evaluate the Impact of Software Tools: A Case Study of the Version Editor , 2002, IEEE Trans. Software Eng..

[6]  Charles W. Butler,et al.  Design complexity measurement and testing , 1989, CACM.

[7]  Daniel M. Germán,et al.  Visualizing the Evolution of Software Using Softchange , 2006, Int. J. Softw. Eng. Knowl. Eng..

[8]  Richard C. Holt,et al.  Predicting change propagation in software systems , 2004, 20th IEEE International Conference on Software Maintenance, 2004. Proceedings..

[9]  Xiaomin Wu,et al.  A reverse engineering approach to support software maintenance: version control knowledge extraction , 2004, 11th Working Conference on Reverse Engineering.

[10]  Daniel M. Germán.  An Empirical Study of Fine-Grained Software Modifications , 2004, ICSM.

[11]  Harvey P. Siy,et al.  If your ver-sion control system could talk , 1997 .

[12]  Andreas Zeller,et al.  Mining Version Histories to Guide Software Changes , 2004 .

[13]  Audris Mockus,et al.  Does Code Decay? Assessing the Evidence from Change Management Data , 2001, IEEE Trans. Software Eng..

[14]  Daniel M. German,et al.  On the use of visualization to support awareness of human activities in software development: a survey and a framework , 2005, SoftVis '05.

[15]  Stéphane Ducasse,et al.  Yesterday's Weather: guiding early reverse engineering efforts by summarizing the evolution of changes , 2004, 20th IEEE International Conference on Software Maintenance, 2004. Proceedings..

[16]  Stephen G. Eick,et al.  Seesoft-A Tool For Visualizing Line Oriented Software Statistics , 1992, IEEE Trans. Software Eng..

[17]  Daniel M. Germán,et al.  A framework for describing and understanding mining tools in software development , 2005, MSR.

[18]  Michael W. Godfrey,et al.  An integrated approach for studying architectural evolution , 2002, Proceedings 10th International Workshop on Program Comprehension.

[19]  Daniel M. Germán,et al.  An empirical study of fine-grained software modifications , 2004, 20th IEEE International Conference on Software Maintenance, 2004. Proceedings..

[20]  Dirk Draheim,et al.  Process-centric analytical processing of version control data , 2003, Sixth International Workshop on Principles of Software Evolution, 2003. Proceedings..