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| E. Merlo, M. Dagenais, P. Bachand, J. S. Sormani, S. Gradara, G. Antoniol, "Investigating Large Software System Evolution: The Linux Kernel," 2012 IEEE 36th Annual Computer Software and Applications Conference, pp. 421, 26th Annual International Computer Software and Applications Conference, 2002. | |||
| BibTex | x | ||
| @article{ 10.1109/CMPSAC.2002.1045038, author = {E. Merlo and M. Dagenais and P. Bachand and J. S. Sormani and S. Gradara and G. Antoniol}, title = {Investigating Large Software System Evolution: The Linux Kernel}, journal ={2012 IEEE 36th Annual Computer Software and Applications Conference}, volume = {0}, year = {2002}, issn = {0730-3157}, pages = {421}, doi = {http://doi.ieeecomputersociety.org/10.1109/CMPSAC.2002.1045038}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE 36th Annual Computer Software and Applications Conference TI - Investigating Large Software System Evolution: The Linux Kernel SN - 0730-3157 SP EP A1 - E. Merlo, A1 - M. Dagenais, A1 - P. Bachand, A1 - J. S. Sormani, A1 - S. Gradara, A1 - G. Antoniol, PY - 2002 KW - software evolution KW - software metrics KW - clone analysis KW - project management VL - 0 JA - 2012 IEEE 36th Annual Computer Software and Applications Conference ER - | |||
Large multi-platform, multi-million lines of codes software systems evolve to cope with new platform or to meet user ever changing needs. While there has been several studies focused on the similarity of code fragments or modules, few studies addressed the need to monitor the overall system evolution. Meanwhile, the decision to evolve or to refactor a large software system needs to be supported by high level information, representing the system overall picture, abstracting from unnecessary details.
This paper proposes to extend the concept of similarity of code fragments to quantify similarities at the release/system level. Similarities are captured by four software metrics representative of the commonalities and differences within and among software artifacts.
To show the feasibility of characterizing large software system with the new metrics, 365 releases of the Linux kernel were analyzed. The metrics, the experimental results as well as the lessons learned are presented in the paper.