Noise Filtering and Imbalance Class Distribution Removal for Optimizing Software Fault Prediction using Best Software Metrics Suite

Joon, Ankush; Tyagi, R. K.; Kumar, Krishan

doi:10.1109/icces48766.2020.9137899

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…Experimental results showed that CHIFS outperformed other methods in terms of accuracy and efficiency, and it was particularly effective when based on the Pearson correlation coefficient. In [64], the researchers used a combination of FS methods to improve the process of predicting software faults. In the study, the FS method Simple Majority Voting was used, which incorporated the results of three different FS methods: Recursive Feature Elimination with Cross-Validation (RFECV), CFS and Select-k-Best FS.…”

Section: A Rq1: Which Feature Selection Methods Are Implemented For S...mentioning

confidence: 99%

“…In [59], three individual classifiers, including SVM, NB and KNN, were implemented to investigate the prediction accuracy of the proposed method. In [64], several classifiers were used, including LR and SVM as individual classifiers and ensemble classifiers such as RF and XGBoost. In [34], [60], [72], two individual classifiers, including DT and NB, were used to evaluate the effectiveness of the proposed method.…”

Section: Embedded Methodsmentioning

confidence: 99%

“…In [32], [61], [65], the researchers employed accuracy, precision, recall, and F-measure to analyze the performance of the proposed FS method. In [62], [64], the effectiveness of the proposed FS and sampling methods was evaluated using precision, recall, f-measure, accuracy, and AUC.…”

Section: Embedded Methodsmentioning

confidence: 99%

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Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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Improving software quality by proactively detecting potential defects during development is a major goal of software engineering. Software defect prediction plays a central role in achieving this goal. The power of data analytics and machine learning allows us to focus our efforts where they are needed most. A key factor in the success of software fault prediction is selecting relevant features and reducing data dimensionality. Feature selection methods contribute by filtering out the most critical attributes from a plethora of potential features. These methods have the potential to significantly improve the accuracy and efficiency of fault prediction models. However, the field of feature selection in the context of software fault prediction is vast and constantly evolving, with a variety of techniques and tools available. Based on these considerations, our systematic literature review conducts a comprehensive investigation of feature selection methods used in the context of software fault prediction. The research uses a refined search strategy involving four reputable digital libraries, including IEEE Explore, Science Direct, ACM Digital Library, and Springer Link, to provide a comprehensive and exhaustive review through a rigorous analysis of 49 selected primary studies from 2014. The results highlight several important issues. First, there is a prevalence of filtering and hybrid feature selection methods. Second, single classifiers such as Naïve Bayes, Support Vector Machine, and Decision Tree, as well as ensemble classifiers such as Random Forest, Bagging, and AdaBoost are commonly used. Third, evaluation metrics such as area under the curve, accuracy, and F-measure are commonly used for performance evaluation. Finally, there is a clear preference for tools such as WEKA, MATLAB, and Python. By providing insights into current trends and practices in the field, this study offers valuable guidance to researchers and practitioners to make informed decisions to improve software fault prediction models and contribute to the overall improvement of software quality.

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Section: A Rq1: Which Feature Selection Methods Are Implemented For S...mentioning

confidence: 99%

Section: Embedded Methodsmentioning

confidence: 99%

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Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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“…In [15], introduces a method for detecting software defects that can help fix a few of the most fundamental issues with current systems. Using a combination of fundamental noise removal, imbalanced class distribution, and software metrics selection methodologies, this study aims to enhance SFP.…”

Section: Related Workmentioning

confidence: 99%

A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset

Gupta,

Sinha

2023

IJRITCC

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The software systems of modern computers are extremely complex and versatile. Therefore, it is essential to regularly detect and correct software design faults. In order to devote resources effectively towards the creation of trustworthy software, software companies are increasingly engaging in the practise of predicting fault-prone modules in advance of testing. These software fault prediction methods rely on the thoroughness with which prior software versions' fault as well as related code has been retrievedTime, energy, and money are all saved as a result. Increases the company's initial success and bottom line greatly by satisfying its clientele. Numerous academics have poured into this area throughout the years in an effort to raise the bar for all software. Nowadays, The most often used approaches in this field are those based on machine learning (ML). The field of ML seeks to perfect software capable of evolving as well as adapting in response to fresh data. This paper introduces a fresh approach for doing ML by bringing together a number of different expert systems. In order to reach agreement on which aspects of a software system need to be tested, the proposed multi-classifier model pools the strengths of the most effective classifiers. Several top-performing classifiers for defect prediction are put through their paces in an experiential evaluation. We test our method on 16 publicly available datasets from the NASA Metric Data Programme (MDP) repository at the promise repository. Parameters of confusion, recall, precision, recognition accuracy, etc., are evaluated and contrasted with existing schemes in a software analysis performed with the help of the python simulation tool with findings. The experimental outcomes demonstrate that by combining LGBM, XGBoost, and Voting classifiers, using a multi classifier approach, we are capable to significantly improve software fault prediction performance. The results of the investigation show that the suggested method will lead to better practical outcomes in the prediction of device failures.

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“…Researchers [57,58,59] have proposed random subsampling [60], SMOTE [61], class balancer [62], and spread subsampling [63] techniques, which help to avoid class imbalance issue and provide unbiased results. Joon et al [64] performed a combined study over class imbalance, feature selection, and simple noise removal strategy over public datasets; they used precision, recall, f-score, roc, and accuracy as performance measures.…”

Section: Class Imbalance Problemmentioning

confidence: 99%