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Unformatted text preview: with the then current proalso hiWhat acan you ajor, but from these graphs? ghly err tic with m deduce d, fluctuations shown in Figure 4 by se to release. Nevertheless, almost Figure 4 S e raa lde r a g e i nv growth trends o f a o the display of very clear trends as particular attribute in Figure 4. T h u s it was natural to regression and autocorrelation techREND VERAGE gression and time-series models to rposes of pSoftware forecasting, and lanning, changes involve whole. A s thewer dy pfewer ssed, evif e stu and rogre number might conoider a sofas are maintes f modules twthe roject a s a software evolves. rganism, self-regulating o m shocks, but-overall -obeying its ws and internal dynamics. • s encouraged the search for models overnedthedynamicbehavior of ation, people, and program material maintenance process, i n the evoluTuesday, September 10, 13 RELEASE SEQUENCE NUMBE Belady & Lehman: the Law of Program Evolution Dynamics 1. Law of continuing change: a system that is used undergoes continuing change until it is judged more cost effective to freeze and recreate it 2. Law of increasing entropy: the entropy of a system (its unstructuredness) increases with time, unless specific work is executed to maintain or reduce it. Tuesday, September 10, 13 Belady & Lehman: the Law of Program Evolution Dynamics 3. Law of statistically smooth growth: Growth trend measures of global system attributes may appear to be stochastic locally in time and space, but statistically, they are cyclically self-regulating, with well-defined longrange trends Tuesday, September 10, 13 Belady & Lehman: the Law of Program Evolution Dynamics • Law of continuing change: a system that is used undergoes continuing change until it is judged more cost effective to freeze and recreate it • ∆M_r = 200 + S1 + Z1 • Law of increasing entropy: the entropy of a system (its unstructuredness) increases with time, unless specific work is executed to maintain or reduce it. • C_r = 0.14 + 0.0012R^2 + S2 + Z2 • Law of statistically smooth growth: Growth trend measures of global system attributes may appear to be stochastic locally in time and space, but statistically, they are cyclically selfregulating, with well-defined long-range trends • M_r = 760 + 200 R + S + Z (where S and Z represents cyclic and stochastic components) Tuesday, September 10, 13 As a skeptic: interesting and valuable to have numbers on how hard it is to change valuable for identifying the timing of refactoring/ rearchitecting / throwing away a system and getting started again. Tuesday, September 10, 13 As a skeptic: • • What is the unit of a module and a component? • • • • What types of changes does each release include? Tuesday, September 10, 13 What is the granularity of a release? Do they have the same amount of functionality addition per each release? Any changes in the organization structures & developers? Are they laws or just hypotheses? What are potential contributions / benefits of understanding software evolution? My general thoughts on Belady & Lehman • • Very insightful paper at the time of 1976 • Discussed the nature of software evolution, characterized it using their empirical data • Deduction of laws from one system’s evolution --- very weak external validity, perhaps hasty conclusions Tuesday, September 10, 13 The rst use of statistical regression for characterizing software evolution Does Code Decay? • • Tuesday, September 10, 13 Eick et al. TSE 2001 (almost 25 years after Belady & Lehman’s Study) Problem Definition • What do the authors mean by “code decay?” • • Tuesday, September 10, 13 it is harder to change than it should be Related to Belady & Lehman’s second law: the entropy of a system increases with time, unless specific work is executed to maintain or reduce it. Discussed Problem • Check whether code decay is real: “Does Code Really Decay?” • • • Tuesday, September 10, 13 how code decay can be characterized the extent to which each risk factor matters *Empirical Study* Paper Hypotheses • What the authors are trying or expecting...
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This document was uploaded on 03/16/2014 for the course EE 360f at University of Texas.

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