Software fault prediction using data reduction approaches

Yohannese, Chubato Wondaferaw; Li, Tianrui; Bashir, Kamal; Simfukwe, Macmillan; Hussein, Ahmed Saad

doi:10.1142/9789813273238_0170

Cited by 3 publications

(1 citation statement)

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“…Naive Bayes was the main classifier in [13], logistic regression used in [7], J48 decision tree used in [14], and random forest used in [21], Bayesian networks used in [15,16], multiple kernel ensemble learning [22], deep learning [23], and semi-supervised deep fuzzy clustering [17]. More recent works applied and proposed different methods for software fault prediction, such as Bayesian networks [15,16], deep learning [24], semi-supervised deep fuzzy clustering [17], faults prediction after reducing irrelevant, redundant features, and using reliable features [25,26], back-propagation neural networks [27], combining genetic algorithms with deep neural network DNN in [23] and with backpropagation learning algorithm in [28], multiple kernel ensemble learning [22], and non-negative sparse graph-based label propagation [29]. In our recent work [20], we explored the effect of g-lasso regression on software fault prediction.…”

Section: Related Workmentioning

confidence: 99%

Explore the effect of size stratification of datasets on software faults proneness prediction performance

Alshehri,

Alshehri

2023

Preprint

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Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explores the effect of size by stratifying our sample based on each unit’s size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project and Derby project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48 and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is significantly better than predicting those with small files. Furthermore, the files of large groups provide reliable and more stable performance. Finally, we resampled all the stratified groups of the two projects to explore if this can impact the performance. We found that the resampling has no impact on the performance of algorithms using the Europa dataset. However, the effect was significant when the Derby project was used.

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Section: Related Workmentioning

confidence: 99%

Explore the effect of size stratification of datasets on software faults proneness prediction performance

Alshehri,

Alshehri

2023

Preprint

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Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

et al. 2022

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The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others. The imbalanced datasets are balanced by applying resampling and various solutions are designed to tackle such datasets that mainly focus on class distribution issues. The imbalanced data is rebalanced using these methods. This paper introduces a technique for balancing data through two stages: first, oversampling methods are utilized in the process of rebalancing such imbalanced dataset using the single-point crossover to generate the new data of minority classes, second, it searches for an optimal subset of the imbalanced and balanced datasets by Jellyfish Search (JS) which is an optimization method. Experiments are performed on 18 real imbalanced datasets, and results are compared with famous oversampling methods and the recently published ACOR (Ant Colony Optimization Resampling) method in terms of different appraisal measurements. Higher performance is recorded by the proposed method and comparability with well-known and recent techniques.

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EBSMOTE: Evaluation-Based Synthetic Minority Oversampling TEchnique for Imbalanced Dataset Learning

Hussein

Diallo

Liu

2019

2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

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Software fault prediction using data reduction approaches

Cited by 3 publications

References 0 publications

Explore the effect of size stratification of datasets on software faults proneness prediction performance

Explore the effect of size stratification of datasets on software faults proneness prediction performance

Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

EBSMOTE: Evaluation-Based Synthetic Minority Oversampling TEchnique for Imbalanced Dataset Learning

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