Developing and maintaining software systems involves a variety of highly interrelated activities The discipline of software engineering studies processes of both product engineering and process engineering Product engineering aims at developing software products of high quality at reasonable cost Process engineering in contrast aims at choosing those product engineering processes appropriate for a given set of project goals and characteristics as well as improving the existing knowledge about those processes Explicit models of both types of processes help a software development organization to gain competitive advantage This paper motivates the need for explicit process models, surveys existing languages to model processes, discusses tools to support model usage, and proposes a research agenda for future software process research

Directions in Software Process Research

Software project control centers: concepts and approaches

It is commonly accepted that all forms of software engineering knowledge should be captured and stored in a repository This knowledge enables us to learn from past development experience and promotes reuse Unfortunately there is not much work in the software engineering literature that emphasizes the importance of capturing implicit knowledge for the repository This paper begins with a critical literature review on the epistemological dimensions of knowledge (explicit and implicit knowledge) in software development Then it speculates a lack of attention on the capture of knowledge for the software experience factory It suggests that the source of implicit knowledge extends beyond the software development team which accumulates implicit knowledge during practice In this paper we formulate a conceptual framework that captures the implicit knowledge, and then organize the knowledge in a useful form for the software experience factory This paper emphasizes the importance of implicit knowledge both on the individual and collective levels and suggests a combination of knowledge capture techniques to achieve this end Though not within the scope of this paper, we recognize that knowledge must be organized in a useful way for easy access

Capturing implicit software engineering knowledge

Improved software quality through improved development process descriptions

The software-development project success dependson the technical competence of the development team,the quality of its tools and the project-managementdecisions it makes during the software lifecycle. Newrequirements, tight delivery schedules and team-memberturnaround present the team with challenges.Flexible decision making for effective adaptation tothese challenges is an extremely difficult skill toacquire, and even more challenging to teach.Instructors of software-engineering courses involvingcollaborative project development are oftenoverwhelmed by the task of monitoring the progress ofmultiple teams and problems in the team's process maygo unnoticed until it is too late to be fixed.In this paper we describe our work on analyzingthe CVS history of a team project repository to extractinformation about the nature of the collaborationbetween the members of a team. This analysis cansupport the instructor in noticing evidence of potentialproblems who can then use this information to alert theteam. It can also be shown to the team membersthemselves, so that they become more aware of theirprocess. We evaluate our CVS analysis process with acase study, based on an undergraduate software-engineeringcourse.

Reverse engineering the process of small novice software teams

All software projects are experiments in the sense of the scientific method because the outcomes are not known in advance, theories and hypotheses may be tested in the project empirically, and data must be collected and analyzed. Empirical data allows us to characterize projects, gauge improvements in an environment, and reason about the value of new technologies. The paper discusses our ideas about practical support for collecting empirical data using a process-sensitive software engineering environment.

H.D. Rombach

Papers

Data collection in a process-sensitive software engineering environment