Paper Info
Title
Empirical evaluation of a fuzzy logic-based software quality prediction model
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.
Year
DOI
Venue
2002
10.1016/S0165-0114(01)00128-2
Fuzzy Sets and Systems
Keywords
Field
DocType
software metrics,inspection data,statistical process control,fuzzy logic-based approach,quality prediction,separate inspection experiment,software inspection,fuzzy logic-based software quality,limited project resource,empirical evaluation,fuzzy logic,error-prone software component,project manager,inspection metric,flexible inference rule,prediction model,software process,satisfiability,software component,software quality,inference rule,software metric
Data mining,Computer science,Fuzzy logic,Software,Statistical process control,Artificial intelligence,Component-based software engineering,Software inspection,Software metric,Software quality,Rule of inference,Machine learning
Journal
Volume
Issue
ISSN
127
2
Fuzzy Sets and Systems
Citations 
PageRank 
References 
16
0.70
18
Authors
3
Name
Order
Citations
PageRank
Sun Sup So1384.20
Sung Deok Cha238129.92
Yong Rae Kwon3103150.37