Empirical evaluation of a fuzzy logic-based software quality prediction model

  • Authors:

  • Sun Sup So;Sung Deok Cha;Yong Rae Kwon

  • Affiliations:

  • Department of Electrical Engineering & Computer Science (EECS), Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon 305-701, South Korea;Department of Electrical Engineering & Computer Science (EECS), Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon 305-701, South Korea;Department of Electrical Engineering & Computer Science (EECS), Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon 305-701, South Korea

  • Venue:
  • Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
  • Year:
  • 2002

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Abstract

Software inspection, due to its repeated success on industrial applications, has now become an industry standard practice. Recently, researchers began analyzing inspection data to obtain insights on how software processes can be improved. For example, project managers need to identify potentially error-prone software components so that limited project resource may be optimally allocated. This paper proposes an automated and fuzzy logic-based approach to satisfy such a need. Fuzzy logic offers significant advantages over other approaches due to its ability to naturally represent qualitative aspect of inspection data and apply flexible inference rules. In order to empirically evaluate the effectiveness of our approach, we have analyzed published inspection data and the ones collected from two separate inspection experiments which we had conducted. χ2 analysis is applied to statistically demonstrate validity of the proposed quality prediction model.