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The past two decades have seen increasing sophistication in software work. Now and in the future, the work of both practitioners and researchers would be helped by a more objective and finer granularity recognition of types of software evolution and software maintenance activities as actually done. To these ends, this paper proposes a clarifying redefinition of the types of software evolution and software maintenance. The paper bases the proposed classification not on people's intentions but upon objective evidence of maintainers' activities ascertainable from observation of activities and artifacts, and/or a before and after comparison of the software documentation. The classification includes taking into account in a semi‐hierarchical manner evidence of the change or lack thereof in: (1) the software, (2) the documentation, (3) the properties of the software, and (4) the customer‐experienced functionality. A comparison is made with other classifications and typologies. The paper provides a classified list of maintenance activities and a condensed decision tree as a summary guide to the proposed evidence‐based classification of the types of software evolution and software maintenance. Copyright © 2001 John Wiley & Sons, Ltd.

This article reports results of a qualitative study that examined communication challenges decision makers experience during the response stage of crisis management. Response is perhaps the most critical of the three stages (prevention, response, recovery) identified in crisis research literature. Response is the point when crisis managers make decisions that may save lives and mitigate the effects of the crisis. Actions at this point also significantly influence public opinion about the crisis and an organization's handling of the event. This study provides additional insight into the complexities of the response stage through analysis of 26 interviews conducted with crisis decision makers involved in 15 organizational crises. Ten additional crises were analyzed through secondary data sources. The result of these analyses is the identification and explication of four crisis response steps: observation, interpretation, choice, and dissemination.

In most software development organizations, there is seldom a one-to-one mapping between software developers and development tasks. It is frequently necessary to concurrently assign individuals to multiple tasks, and to assign more than one individual to work cooperatively on a single task. A principal goal in making such assignments should be to minimize the effort required to complete each task. But what impact does the manner in which developers are assigned to tasks have on the effort requirements? This paper identifies four task assignment factors: team size, concurrency, intensity and fragmentation. These four factors are shown to improve the predictive ability of the well-known Intermediate COCOMO cost estimation model. A parsimonious effort estimation model is also derived that utilizes a subset of the task assignment factors and Unadjusted Function Points. For the data examined, this parsimonious model is shown to have goodness of fit and quality of estimation superior to that of the COCOMO model, while utilizing fewer cost factors.

The Department of Information Systems, Statistics and Management Science, prediction of software defects and defect patterns is and will continue to be a critically important software evolution research topic. This study presents a time series analysis of multi‐organizational multi‐project defects reported during ongoing software evolution efforts. Using data from monthly defect reports for eight open source software projects over five years, this study builds and tests time series models for each sampled project. The resulting model accounts for the ripple effects of defect detection and correction by modeling the autocorrelation of code defect data. The autoregressive integrated moving average model (0,1,1) was found to hold for all sampled projects and thus provide a basis for both descriptive and predictive software defect analysis that is computationally efficient, comprehensible, and easy to apply. The model may be used to evaluate and compare the reliability of candidate software solutions, and to facilitate planning for software evolution budget and time allocation. Copyright © 2008 John Wiley & Sons, Ltd.