  Planning quality assurance (QA) activities in a systematic way and
controlling their execution are challenging tasks for companies that develop
software or software-intensive 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 that combines
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
cross-validation is 29.6% compared to 76.5% obtained by the most accurate
data-based model.
