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Software quality prediction using fuzzy integration: a case study

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Abstract

Given the complexity of many contemporary software systems, it is often difficult to gauge the overall quality of their underlying software components. A potential technique to automatically evaluate such qualitative attributes is to use software metrics as quantitative predictors. In this case study, an aggregation technique based on fuzzy integration is presented that combines the predicted qualitative assessments from multiple classifiers. Multiple linear classifiers are presented with randomly selected subsets of automatically generated software metrics describing components from a sophisticated biomedical data analysis system. The external reference test is a software developer’s thorough assessment of complexity, maintainability, and usability, which is used to assign corresponding quality class labels to each system component. The aggregated qualitative predictions using fuzzy integration are shown to be superior to the predictions from the respective best single classifiers.

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Authors and Affiliations

  1. Institute for Biodiagnostics, National Research Council of Canada, 435 Ellice Avenue, Winnipeg, MB, R3B 1Y6, Canada

    Nick J. Pizzi

  2. Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada

    Nick J. Pizzi

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  1. Nick J. Pizzi
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Correspondence to Nick J. Pizzi.

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Pizzi, N.J. Software quality prediction using fuzzy integration: a case study. Soft Comput 12, 67–76 (2008). https://doi.org/10.1007/s00500-007-0217-4

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