Evelyn J. Barry

Software Project Duration and Effort: An Empirical Study

Software projects frequently finish late and over budget. Much of the research to date has characterized this problem in terms of inadequate project estimation or incomplete requirements determination. In this study, we concentrate instead on understanding the relationship between project duration and project effort. Over time, a dynamic environment contributes to the expansion of project requirements, thus increasing the scope and effort required to complete the project, irrespective of initial requirements and anticipated project size. Further, frequent delays and interruptions in a project contribute to greater effort each time work is resumed. We develop and empirically evaluate a two-stage model to relate project duration and effort. Our results indicate a significant and positive relationship between project duration and effort, controlling for anticipated project size and other project characteristics. Our model also provides an estimate for the rate of environmental change while projects are in progress. We demonstrate the practical implications of our model by showing how it can be used in conjunction with time boxing techniques and new development methodologies to better scope software projects.

On the uniformity of software evolution patterns

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 portfolios 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.

Environmental Volatility, Development Decisions, and Software Volatility: A Longitudinal Analysis

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 the challenges are 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 20 years to a firms environment, its managers development choices, and its software products. In Stage 1 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 2 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.

How software process automation affects software evolution: a longitudinal empirical analysis

SUMMARY 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, weuse data from source control systems, defect tracking systems, andarchived 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 c � 2007 John Wiley & Sons, Ltd.

Software evolution, volatility and lifecycle maintenance patterns: a longitudinal analysis synopsis

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.