Statistical analysis of nonstationary software metrics

Prediction, estimation, and assessment of software process attributes form an integral part of process management. Process modeling is a quantitative and systematic approach to gauging such critical project parameters. However, process modeling relies heavily on idealizations. Mathematical compromises necessitate continuous calibration of such models to maintain some level of accuracy. This is because of the nature of software measurement data, most of which are nonstationary, and thus requires special treatment. A methodology for analyzing such data is presented in this paper. It is shown that measures based on time averages are insufficient in representing data of this nature. The improved representativeness of ensemble based measures over those based on time is demonstrated. A model for LOC generation, based on ensemble averaging, is presented in this paper. However, the process of validating such a model involves testing the model to a range of unique inputs. But, exhaustive testing of process models is generally severely constrained by the lack of sufficient amounts of input data sets, generated under the conditions imposed by the modeling approach. A method employing the random walk, by which an ensemble can be generated from a single time record, on the basis of certain invariants, is provided. This method of generating an ensemble is shown to be useful, especially in situations where certain properties of the proprietary data are known, but sufficient quantities of the data are inaccessible to the analyst. The ensemble computed in this manner is then used to derive a time dependent model that is more representative of the real life entity being modeled. (C) 1997 Elsevier Science B.V.