Abstract-Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the project Quamoco, we built a comprehensive approach with the aim to close this gap.For this, we developed in several iterations a meta quality model specifying general concepts, a quality base model covering the most important quality factors and a quality assessment approach. The meta model introduces the new concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects. Product factors have measures and instruments to operationalise quality by measurements from manual inspection and tool analysis. The base model uses the ISO 25010 quality attributes, which we refine by 200 factors and 600 measures for Java and C# systems.We found in several empirical validations that the assessment results fit to the expectations of experts for the corresponding systems. The empirical analyses also showed that several of the correlations are statistically significant and that the maintainability part of the base model has the highest correlation, which fits to the fact that this part is the most comprehensive. Although we still see room for extending and improving the base model, it shows a high correspondence with expert opinions and hence is able to form the basis for repeatable and understandable quality assessments in practice.

Context Software quality models provide either abstract quality characteristics or concrete quality measurements; there is no seamless integration of these two aspects. Quality assessment approaches are, hence, also very specific or remain abstract. Reasons for this include the complexity of quality and the various quality profiles in different domains which make it difficult to build operationalised quality models. Objective In the project Quamoco, we developed a comprehensive approach aimed at closing this gap. Method The project combined constructive research, which involved a broad range of quality experts from academia and industry in workshops, sprint work and reviews, with empirical studies. All deliverables within the project were peer-reviewed by two project members from a different area. Most deliverables were developed in two or three iterations and underwent an evaluation. Results We contribute a comprehensive quality modelling and assessment approach: (1) A meta quality model defines the structure of operationalised quality models. It includes the concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects, and allows modularisation to create modules for specific domains. (2) A largely technology-independent base quality model reduces the effort and complexity of building quality models for specific domains. For Java and C# systems, we refined it with about 300 concrete product factors and 500 measures. (3) A concrete and comprehensive quality assessment approach makes use of the concepts in the meta-model. (4) An empirical evaluation of the above results using real-world software systems showed: (a) The assessment results using the base model largely match the expectations of experts for the corresponding systems. (b) The approach and models are well understood by practitioners and considered to be both consistent and well suited for getting an overall view on the quality of a software product. The validity of the base quality model could not be shown conclusively, however. (5) The extensive, open-source tool support is in a mature state. (6) The model for embedded software systems is a proof-of-concept for domain-specific quality models. Conclusion We provide a broad basis for the development and application of quality models in industrial practice as well as a basis for further extension, validation and comparison with other approaches in research

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Planning quality assurance (QA) activities in a systematic way and controlling their execution are challenging tasks for companies that develop software or softwareintensive systems. Both require estimation capabilities regarding the effectiveness of the applied QA techniques and the defect content of the checked artifacts. Existing approaches for these purposes need extensive measurement data from historical projects. Due to the fact that many companies do not collect enough data for applying these approaches (especially for the early project lifecycle), they typically base their QA planning and controlling solely on expert opinion. This article presents a hybrid method combining commonly available measurement data and context-specific expert knowledge. To evaluate the method's applicability and usefulness, we conducted a case study in the context of independent verification and validation activities for critical software in the space domain. A hybrid defect content and effectiveness model was developed for the software requirements analysis phase and evaluated with available legacy data. One major result is that the hybrid model provides improved estimation accuracy when compared to applicable models based solely on data. The mean magnitude of relative error (MMRE) determined by crossvalidation is 29.6% compared to 76.5% obtained by the most accurate data-based model.