SUMMARY As software evolution organizations (SEOs) formalize their activities into standard repeatable processes, capability maturity models suggest that they gather and analyze data to quantitatively manage their activities. This study examines the use of a time‐series cross‐section (TSCS) modeling approach to inform the quantitative management of the testing activities within a mature SEO. This paper describes the TSCS modeling approach and provides an exemplar case study that details its use. Data for the case study come from readily available, commonly gathered production and testing defect reports in the quantitative measurement efforts of ait CMMI‐DEV Level 3 assessed software development and evolution organization. Covering six independent projects for 43 months, the case study defines quality in terms of reported defects in the production code base. The predictors are the six projects and defect data identified in the unit, system, and regression testing activities logs. The case study also details how results from the TSCS analysis triggered causal analysis to improve the testing activities. The study provides evidence that TSCS models can be used as a quantitative problem identification tool for the mature (SMmmLevel 3 or higher) SEO's testing activities. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract 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.

Abstract 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.

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 field of component-based software engineering (CBSE) is currently in a period of rapid growth and change. No longer is the field dominated by discussions of traditional kinds of source-code components, but the predominant view encompasses so-called "binary" components: components that adhere to industry standards and are shipped as "plug-ins" into an existing run-time architecture. Computer science and software engineering curricula have not yet caught up with this transition in providing a corpus of fundamentally sound concepts to support education within this discipline. In this paper, we describe our efforts to develop fundamentals-driven CBSE educational materials, as well as our efforts to integrate these materials into a traditional undergraduate computer science curriculum.

This study empirically evaluates Hale and Haworth's cognitive processes model for programmers engaged in software debugging. They claim that across all levels of experience, software programmers engage in the same activities and follow a similar pattern of activities directed by search strategies. Verbal protocol analysis of data gathered during a staged problem-solving episode (e.g., fixing a hidden bug) was used to evaluate Hale and Haworth's hypothesized debugging tasks, as well as the hypothesized sequence in which the tasks are performed. Empirical support for Hale and Haworth's model was strong. Specifically, the set of hypothesized debugging tasks is both accurate and complete. Further, the model effectively represents the process by which programmers generate and evaluate hypotheses, and the manner in which programmers recursively attack a debugging problem. Unsupported are those complex debugging sequences involving multiple possible paths. This study extends the understanding of the mechanisms by which programmers choose, implement and evaluate debugging rules and strategies. Results verify that all observed programmers utilize a pattern of cognitive process steps as well as comprehension strategies to derive needed declarative knowledge. The results support a goal-driven model of program debuggers' cognitive processes depicting a hierarchy of levels with an inherent control structure that directs debugging process steps. Copyright © 1999 John Wiley & Sons, Ltd.

This paper describes the development of a new software engineering curriculum at the University of Alabama. This curriculum is jointly administered by three separate academic programs: computer engineering, computer science and management information systems. These programs have combined to develop a unified software engineering track, consisting of an optional sequence of courses for majors in the three programs. Our software engineering track is characterized by two key elements: the use of software component engineering as a technical foundation, and the integration of students from the three participating programs into a single capstone laboratory. The capstone laboratory involves the development of a multifaceted product, requiring the interdisciplinary cooperation of all three groups of students to complete. This paper describes the design and conceptual foundations of this program, which has received funding from the National Science Foundation.