Does code decay? Assessing the evidence from change management data Export

@article{citeulike:356159, abstract = {A central feature of the evolution of large software systems is that change-which is necessary to add new functionality, accommodate new hardware, and repair faults-becomes increasingly difficult over time. We approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteen-plus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed}, author = {Eick, S. G. and Graves, T. L. and Karr, A. F. and Marron, J. S. and Mockus, A.}, citeulike-article-id = {356159}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=895984}, journal = {Software Engineering, IEEE Transactions on}, keywords = {aging, architecture, code, decay, software, software-architecture, software-evolution, systems}, number = {1}, pages = {1--12}, posted-at = {2007-09-26 14:53:14}, priority = {0}, title = {Does code decay? Assessing the evidence from change management data}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=895984}, volume = {27}, year = {2001} }

TY - JOUR ID - citeulike:356159 L3 - citeulike-article-id:356159 N2 - A central feature of the evolution of large software systems is that change-which is necessary to add new functionality, accommodate new hardware, and repair faults-becomes increasingly difficult over time. We approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteen-plus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed IS - 1 JF - Software Engineering, IEEE Transactions on EP - 12 TI - Does code decay? Assessing the evidence from change management data VL - 27 SP - 1 KW - aging KW - architecture KW - code KW - decay KW - software KW - software-architecture KW - software-evolution KW - systems AU - Eick, SG AU - Graves, TL AU - Karr, AF AU - Marron, JS AU - Mockus, A PY - 2001/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=895984 ER -