Support planning and controlling of early quality assurance by combining expert judgment and defect data--a case study

  • Authors:

  • Michael Kläs;Haruka Nakao;Frank Elberzhager;Jürgen Münch

  • Affiliations:

  • Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany 67663;Safety & Product Assurance Department, Japan Manned Space Systems Corporation, Tsuchiura, Japan 300-0033;Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany 67663;Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany 67663

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

Abstract

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 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 cross-validation is 29.6% compared to 76.5% obtained by the most accurate data-based model.