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Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering

Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering

Golnoush Abaei, Ali Selamat
Copyright: © 2014 |Volume: 2 |Issue: 4 |Pages: 12
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781466656628|DOI: 10.4018/ijsi.2014100105
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MLA

Abaei, Golnoush, and Ali Selamat. "Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering." IJSI vol.2, no.4 2014: pp.60-71. http://doi.org/10.4018/ijsi.2014100105

APA

Abaei, G. & Selamat, A. (2014). Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering. International Journal of Software Innovation (IJSI), 2(4), 60-71. http://doi.org/10.4018/ijsi.2014100105

Chicago

Abaei, Golnoush, and Ali Selamat. "Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering," International Journal of Software Innovation (IJSI) 2, no.4: 60-71. http://doi.org/10.4018/ijsi.2014100105

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Abstract

Despite proposing many software fault prediction models, this area has yet to be explored as still there is a room for stable and consistent model with better performance. In this paper, a new method is proposed to increase the accuracy of fault prediction based on the notion of fuzzy clustering and majority ranking. The authors investigated the effect of irrelevant and inconsistent modules on software fault prediction and tried to decrease it by designing a new framework, in which the entire project modules are clustered. The obtained results showed that fuzzy clustering could decrease the negative effect of irrelevant modules on prediction performance. Eight data sets from NASA and Turkish white-goods software is employed to evaluate our model. Performance evaluation in terms of false positive rate, false negative rate, and overall error showed the superiority of our model compared to other predicting models. The authors proposed majority ranking fuzzy clustering approach showed between 3% to 18% and 1% to 4% improvement in false negative rate and overall error, respectively, compared with other available proposed models (ACF and ACN) in more than half of the testing cases. According to the results, our systems can be used to guide testing effort by identifying fault prone modules to improve the quality of software development and software testing in a limited time and budget.

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