Although product development research often focuses on activities prior to product launch, for long-lived, adaptable products like software, development can continue over the entire product life cycle. For managers of these products a challenge is to predict when and how much the products will change, and to understand how their development decisions influence the timing and magnitude of future change activities. We develop a two-stage model that relates environmental volatility to product development decisions, and product development decisions to software volatility. The model is evaluated using a data archive that captures changes over twenty years to a firm's environment, its managers' development choices, and its software products. In stage one we find that higher environmental volatility leads to greater use of process technology and standard component designs, but less team member rotation. Earlier development decisions strongly influence current development choices, especially for product design and process technology. In stage two we find that increased use of standard component designs dampens future software volatility by decreasing the average rate and magnitude of change. Adding new team members increases product enhancements at a faster pace than more intense use of process technology, but adds repairs at almost the same rate as enhancements.

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.

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