An empirical approach to studying software evolution

doi:10.1109/32.799945

1999-01-01

188

PRE

Chris F. Kemerer *

Sandra A. Slaughter

DOI：10.1109/32.799945

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摘要

En 中文

With the approach of the new millennium, a primary focus in software engineering involves issues relating to upgrading, migrating, and evolving existing software systems. In this environment, the role of careful empirical studies as the basis for improving software maintenance processes, methods, and tools is highlighted. One of the most important processes that merits empirical evaluation is software evolution. Software evolution refers to the dynamic behavior of software systems as they are maintained and enhanced over their lifetimes. Software evolution is particularly important as systems in organizations become longer-lived. However, evolution is challenging to study due to the longitudinal nature of the phenomenon in addition to the usual difficulties in collecting empirical data. In this paper, we describe a set of methods and techniques that we have developed and adapted to empirically study software evolution. Our longitudinal empirical study involves collecting, coding, and analyzing more than 25,000 change events to 23 commercial software systems over a 20-year period. Using data from two of the systems, we illustrate the efficacy of flexible phase mapping and gamma sequence analytic methods originally developed in social psychology to examine group problem solving processes. We have adapted these techniques in the context of our study to identify and understand the phases through which a software system travels as it evolves over time. We contrast this approach with time series analysis, the more traditional way of studying longitudinal data. Our work demonstrates the advantages of applying methods and techniques from other domains to software engineering and illustrates how, despite difficulties, software evolution can be empirically studied.

Keyword:

empirical methods

software evolution

software maintenance

longitudinal studies

time series

sequence analysis

gamma analysis

phasic analysis