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

Sun Sup So, Sung Deok Cha, Yong Rae Kwon

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)199-208
Number of pages10
JournalFuzzy Sets and Systems
Issue number2
Publication statusPublished - 2002 Apr 16
Externally publishedYes


  • Fuzzy logic
  • Inspection metric
  • Quality prediction
  • Software inspection
  • Software metrics
  • Statistical process control

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence


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