Towards a Theoretical Model for Software Growth
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Software evolution in open source projects—a large-scale investigation
Journal of Software Maintenance and Evolution: Research and Practice
Characterizing software architecture changes: A systematic review
Information and Software Technology
User generated (web) content: trash or treasure
Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution
Perpetual development: A model of the Linux kernel life cycle
Journal of Systems and Software
Users and developers: an agent-based simulation of open source software evolution
SPW/ProSim'06 Proceedings of the 2006 international conference on Software Process Simulation and Modeling
Size doesn't matter?: on the value of software size features for effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Preliminary lessons from a software evolution analysis of Moodle
Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality
ACM Computing Surveys (CSUR)
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There are some concerns in the research community about the convenience of using low-level metrics (such as SLOC, source lines of code) for characterizing the evolution of software, instead of the more traditional higher lever metrics (such as the number of modules or files). This issue has been raised in particular after some studies that suggest that libre (free, open source) software evolves differently than 'traditional' software, and therefore it does not conform to Lehman's laws of software evolution. Since those studies on libre software evolution use SLOCs as the base metric, while Lehman's and other traditional studies use modules or files, it is difficult to compare both cases. To overcome this difficulty, and to explore the differences between SLOC and files/modules counts in libre software projects, we have selected a large sample of programs and have calculated both size metrics over time. Our study shows that in those cases the evolution patterns in both cases (counting SLOCs or files) is the same, and that some patterns not conforming to Lehman's laws are indeed apparent.