Optimized Horizontal and Vertical Dimension Selection using Hybrid Sampling and Quadratic Discriminant Analysis for Predicting Software Faults

Optimized Horizontal and Vertical Dimension Selection using Hybrid Sampling and Quadratic Discriminant Analysis for Predicting Software Faults
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© 2025 by IJETT Journal
Volume-73 Issue-6
Year of Publication : 2025
Author : Yuvaraj K, Balaji N V
DOI : 10.14445/22315381/IJETT-V73I6P127

How to Cite?
Yuvaraj K, Balaji N V, "Optimized Horizontal and Vertical Dimension Selection using Hybrid Sampling and Quadratic Discriminant Analysis for Predicting Software Faults," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.318-335, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P127

Abstract
Software fault prediction is significant research intended to ascertain the faults in the software modules by analysing their various parameters. It aims to ensure maximum quality with minimum time, effort, cost, and usage of testing resources for the underlying software. Like any application, the quality of the data prominently stimulates the prediction result of the software fault. Intrinsically, several challenges, such as class imbalance, irrelevant and redundant attributes, and instance noise, exist in the software defect datasets. This irrelevant input slows the underlying prediction model's performance and produces erroneous prediction results. A data preprocessing methodology has been presented to address this problem by properly choosing the vertical and horizontal dimensions to ensure the quality of the input data. To handle data imbalance in the horizontal dimensions, hybrid sampling that uses SMOTE for oversampling and random under-sampling is applied to the data. It also uses the edited k nearest neighbour rule to remove noises. On the other hand, significant attributes from the vertical dimensions of the dataset are identified by applying the quadratic discriminant analysis. Several datasets have been used in the experimental study to assess the suggested preprocessing model's performance. The findings show that the suggested model performs better as it maintains the quality of the pre-processed dataset. The comparative analysis ensures that the suggested model overcomes the difficulties and performs well enough to forecast software module defects with improved quality up to 2.6% to 5.2% of AuC values.

Keywords
Class imbalance, Edited k nearest neighbour rule, Quadratic discriminant analysis, Random sampling, Software defects, Software fault prediction.

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