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<p><b>Abstract</b>—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. In this paper, we approach this phenomenon, which we term <it>code decay</it>, 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.</p>
Software maintenance, metrics, statistical analysis, fault potential, span of changes, effort modeling.
Alan F. Karr, Audris Mockus, Todd L. Graves, Stephen G. Eick, J.s. Marron, "Does Code Decay? Assessing the Evidence from Change Management Data", IEEE Transactions on Software Engineering, vol. 27, no. , pp. 1-12, January 2001, doi:10.1109/32.895984
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