Abstract This research analyzes longitudinal empirical data on commercial software applications to test and better understand how software evolves over time, and to measure the likely long‐term effects of a software process automation tool on software productivity and quality. The research consists of two parts. First, we use data from source control systems, defect tracking systems, and archived project documentation to test a series of hypotheses developed by Belady and Lehman about software evolution. We find empirical support for many of these hypotheses, but not all. We then further analyze the data using moderated regression analysis to discern how software process automation efforts at the research site influenced the software evolution lifecycles of the applications. Our results support the claim that automation has enabled the organization to accomplish more work activities with greater productivity, thereby significantly increasing the functionality of the applications portfolio. Despite the growth in software functionality, the analysis suggests that automation has helped to manage software complexity levels and to improve quality by reducing errors over time. Our models and their results demonstrate how longitudinal empirical software data can be used to reveal the often elusive long‐term benefits of investments in software process improvement, and to help managers make more informed resource‐allocation decisions. Copyright © 2007 John Wiley & Sons, Ltd.

Preparations for Y2K reminded the software engineering community of the extent to which long-lived software systems are embedded in our daily environments. As systems are maintained and enhanced throughout their lifecycles they appear to follow generalized behaviors described by the laws of software evolution. Within this context, however, there is some question of how and why systems may evolve differently. The objective of this work is to answer the question: do systems follow a set of identifiable evolutionary patterns? In this paper we use software volatility to describe the lifecycle evolution of a portfolio of 23 software systems. We show by example that a vector of software volatility levels can represent lifecycle behavior of a software system. We further demonstrate that the portfolio's 23 software volatility vectors can be grouped into four distinguishable patterns. Thus, we show by example that there are different patterns of system lifecycle behavior, i.e. software evolution.

Despite the rapidity of technological change it is still true that many software systems remain productive for decades. To stay current these systems must evolve as they age. How can these lifecycle software changes, i.e. software volatility, be conceptualized and measured? What are the antecedents of software volatility? How do software volatility and lifecycle maintenance patterns affect lifecycle maintenance outcomes? This research defines and evaluates a system-level multi-dimensional measure of software volatility. Longitudinal analyses use a panel dataset built from a 20-year log of software modifications to 23 application systems. Contributions from this work include a multi-dimensional measure of software volatility, identification of antecedents of volatility and evidence that software volatility and lifecycle maintenance patterns can predict future maintenance outcomes.