Today’s software developments are faced with steadily increasing expectations: software has to be developed faster, better, and cheaper. At the same time, application complexity increases. Meeting these demands requires fast, continuous learning and the reuse of past experience on the part of the project teams. Thus, learning and reuse should be supported by well-defined processes applicable to all kinds of experience which are stored in an organizational memory. In this paper, we introduce a tool architecture supporting continuous learning and reuse of all kinds of experience from the software engineering domain and present the underlying methodology. 1.

Deduction-based software component retrieval uses preand postconditions as indexes and search keys and an automated theorem prover (ATP) to check whether a component matches. This idea is very simple but the vast number of arising proof tasks makes a practical implementation very hard. We thus pass the components through a chain of filters of increasing deductive power. In this chain, rejection filters based on signature matching and model checking techniques are used to rule out non-matches as early as possible and to prevent the subsequent ATP from "drowning." Hence, intermediate results of reasonable precision are available at (almost) any time of the retrieval process. The final ATP step then works as a confirmation filter to lift the precision of the answer set. We implemented a chain which runs fully automatically and uses MACE for model checking and the automated prover SETHEO as confirmation filter. We evaluated the system over a medium-sized collection of components. The resul...

. Reuse of software knowledge is a principle for improving productivity and reliability of software development. To achieve this, reuse must be done systematically. This means that processes for retrieving, reusing, revising, and retaining have to be defined. At the same time organizational issues (such as the establishment of a separate organizational unit responsible for organizational learning) must be considered. In this paper we compare software knowledge reuse models to the CBR cycle of Aamodt and Plaza [1] and show that the approaches are very similar. We suggest to extend the CBR cycle by including organizational issues explicitly and conclude that CBR is a promising technology for realizing software knowledge reuse if our suggested organizational extensions are considered. Keywords. Organizational View on CBR, Organizational Learning, Experience Factory, Quality Improvement Paradigm, Software Knowledge Reuse 1 Introduction Reuse practice appears to exhibit consid...

Today's software developments are faced with steadily increasing expectations: software has to be developed faster, better, and cheaper. At the same time, application complexity increases. Meeting these demands requires fast, continuous learning and the reuse of experience on the part of the project teams. Thus, learning and reuse should be supported by well-defined processes applicable to all kinds of experience which are stored in an organizational memory. In this paper, we introduce a tool architecture supporting continuous learning and reuse of all kinds of experience from the software engineering domain and present the underlying methodology.

For the improvement of software quality and productivity, organizations need to systematically build up and reuse software engineering know-how, promoting organizational learning in software development. Therefore, an integrated support platform has to be developed for capturing, storing and retrieving software engineering knowledge. Technical support is complicated through specific characteristics of the software engineering domain, such as the lack of explicit domain models in practice and the diversity of environments. Applying Case-Based Reasoning, we propose an approach for the representation of relevant software engineering experiences, the goal-oriented and similarity-based retrieval tailorable to organization-specific characteristics and the continuous acquisition of new experiences. The approach is applied and validated in the context of the Goal/Question/Metric (GQM) approach, an innovative technology for software measurement.