Software Defect Prediction using KPCA & CSANFIS
Main Article Content
Abstract
Identifying Bugs/Defects in the early stages of software life cycle reduces the effort required in software development. A lot of research has been progressed in predicting software defects using machine learning approaches. In software defect prediction, there are mainly two problems, dimensionality reduction and class Iimbalance. In this paper, we are addressing dimensionality reduction using Kernal Principle Component Analysis and Class Imbalance problem using Cost sensitive Class Imbalance Problem. Kernal Principle Component Analysis transforms non linear high dimensional data into low dimensional space.Cost Sensitive Adaptive Neuro Fuzzy Inference System assigns weights to samples based on class imbalance ratio to alleviate biasing in classification towards majority class. The performance of proposed methodology is measured using Area under ROC Curve (AuC) values. We performed experimentation on Software Defect datasets downloaded from NASA Dataset repository and observed Auc values are increased with our proposed methodology by 5-6%.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.