Goal-oriented Composition of Software Process Patterns

The development of high-quality software or software-intensive systems requires custom-tailored process models that fit the organizational and project goals as well as the development contexts. These models are a necessary prerequisite for creating project plans that are expected to fulfill business goals. Although project planners require individual process models custom-tailored to their constraints, software or system developing organizations also require generic processes (i.e., reference processes) that capture project-independent knowledge for similar development contexts. The latter is emphazised by assessment approaches (such as CMMI, SPICE) that require explicit process descriptions in order to reach a certain capability or maturity level. Among other concepts such as polymorphism, templates, or generator-based descriptions, software process patterns are used to describe generic process knowledge. Several approaches for describing the architecture of process patterns have already been published (e.g., [7]). However, there is a lack of descriptions on how to compose process patterns for a specific decelopment context in order to gain a custom-tailored process model for a project. This paper focuses on the composition of process patterns in a goal-oriented way. First, the paper describes which information a process pattern should contain so that it can be used for systematic composition. Second, a composition method is sketched. Afterwards, the results of a proof-of-concept evaluation of the method are described. Finally, the paper is summarized and open research questions are sketched.Comment: 5 page

Adapting Software Quality Models: Practical Challenges, Approach, and First Empirical Results

Measuring and evaluating software quality has become a fundamental task. Many models have been proposed to support stakeholders in dealing with software quality. However, in most cases, quality models do not fit perfectly for the target application context. Since approaches for efficiently adapting quality models are largely missing, many quality models in practice are built from scratch or reuse only high-level concepts of existing models. We present a tool-supported approach for the efficient adaptation of quality models. An initial empirical investigation indicates that the quality models obtained applying the proposed approach are considerably more consistently and appropriately adapted than those obtained following an ad-hoc approach. Further, we could observe that model adaptation is significantly more efficient (~factor 8) when using this approach.Comment: 8 pages. The final publication is available at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=606836