A Hybrid Neuro Fuzzy Approach for Bug Prediction using Software Metrics

Aditi Thakur; Dr. Ajay Goel

doi:https://doi.org/10.14445/22315381/IJETT-V38P217

Research Article | Open Access | Download PDF

Volume 38 | Number 1 | Year 2016 | Article Id. IJETT-V38P217 | DOI : https://doi.org/10.14445/22315381/IJETT-V38P217

A Hybrid Neuro Fuzzy Approach for Bug Prediction using Software Metrics

Aditi Thakur, Dr. Ajay Goel

Citation :

Aditi Thakur, Dr. Ajay Goel, "A Hybrid Neuro Fuzzy Approach for Bug Prediction using Software Metrics," International Journal of Engineering Trends and Technology (IJETT), vol. 38, no. 1, pp. 85-92, 2016. Crossref, https://doi.org/10.14445/22315381/IJETT-V38P217

Abstract

Software quality is an important factor since software systems are playing a key role in today’s world. There are several perspectives within the field on software quality measurement. This measurement is frequently used so many defects which can cause crashes, failures, or security breaches encountered in the software. Testing the software for such defect is essential to enhance the quality. However, due to the increase in intricacy of software manual testing was becoming extremely time consuming task and some automatic supporting tools have been developed. One such supporting tool is defect prediction models. Some defect prediction models can be found in the literature and most of them share a common procedure to develop the models. In general, the models’ development procedure indirectly assumes that underlying data distribution of software systems is relatively stable over time. But, this assumption is not necessarily true and consequently, the reliability of those models is doubtful at some points in time.

Keywords

feature selection, ANFIS, LDA, parameters, approaches.

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