Selecting best attributes for software defect prediction

Mandal, Prianka; Ami, Amit Seal

doi:10.1109/wiecon-ece.2015.7444011

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…Mandal and Ami were the ones who initially provided the attribute selection for the SDP model [12]. The findings of the experiment indicate that the concept that was provided offers a collection of qualities that is nearly as excellent as the ideal set, which contributes to the improvement in the performance of the defect prediction model.…”

Section: Feature Selection Techniques For Software Fault Predictionmentioning

confidence: 99%

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

et al. 2023

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The amount of time and money required to finish a software project and distribute the final product increases when there are bugs in the programme. Software procedures like defect monitoring and repair may be both costly and time-consuming to complete. Because it is difficult to locate and correct every defect in a product, it is essential that the negative effect of those defects be minimised in order to provide a result that is of better overall quality. The process of identifying troublesome sections of software code is known as software defect prediction. This paper presents an optimized machine learningenabled model for software fault prediction to improve software quality. PC1 data set is fed as input data in this model. Important features are selected by ant colony optimization (ACO) technique. Selected features are fed as input to support vector machine. Training and testing of SVM is performed by PC1 data set. Performance of ACO SVM Ant Colony Optimization Support Vector Machine is compared with SVM, Naive Bayes classifier and K-Nearest Neighbour classifier. The performance of ACO-based SVM is better for software fault classification and prediction.

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Section: Feature Selection Techniques For Software Fault Predictionmentioning

confidence: 99%

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

et al. 2023

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“…In [4], the researchers introduced a comprehensive attribute selection process consisting of five consecutive steps. First, they calculated the balance of each attribute using a base classifier and then ranked the attributes in a list based on their respective balance values.…”

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

confidence: 99%

“…Researchers proposed several frameworks, in- Proposed an FS algorithm based on feature combinations, sorting, and selecting the best feature set [2] Proposed an FS framework based on feature clustering and feature ranking called FECAR [38] Proposed an FS method named relief-LC algorithm by seamlessly combining relief FS and Linear Correlation analysis 2015 [24] Proposed an average probability ensemble (APE) method using greedy forward selection (GFS) and correlation-based FS methods [3] Proposed a clustering-based FS method using maximal information coefficient [7] Explored ten filter/wrapper-based FS methods on sixteen open-source projects. [4] Proposed a comprehensive FS method calculating the balance, frequency and weight of each attribute 2016 [10] Propose a sequential FS method by calculating and iteratively optimizing the balance of each attribute [5] Proposed an optimized FS method using Correlation and ANOVA [4] Proposed MICHAC (Maximal Information Coefficient with Hierarchical Agglomerative Clustering) framework, using MIC and Hierarchical Agglomerative Clustering (HAC) methods for FS [53] Proposed a framework using chisquaredattributeeval and correlation attribute eval along with best first, greedy stepwise, and Hierarchical Agglomerative Clustering (HAC) [3] Proposed FS method through General Linear Model (GLM) regression to assess variable importance (VI) for each feature 2017 [41] Proposed an FS method using density-based clustering of hybrid data and ranking strategies [42] Proposed a method for FS using bat-based search along with Correlation-based Feature Selection (CFS) [40] Integrated the SMOTE sampling technique with the three feature selection algorithms, including Chi-Square (CS), Information Gain (IG), and Relief (RLF) [29] Proposed a multi-objective feature selection method based on optimization principles [28] Proposed a FS method based on a similarity measure (SM) [27] Designed a parallel hybrid framework for FS that combined both filter and wrapper methods [8] Proposed a hybrid method for FS using filter and wrapper methods [37] Proposed FESCH (Feature Selection Using Clusters of Hybrid-Data) for Cross-Project Defect Prediction (CPDP) [60] Proposed correlation-based FS method 2018 [44] Proposed a hybrid FS method using wrapper and filter methods by implementing a fast correlation-based filter method 2019 [45] Proposed an FS method using three wrapper algorithms, including Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimiza...…”

Section: ) Embedded Methodmentioning

confidence: 99%

“…In [7], the researchers implemented a mixture of individual and ensemble classifiers by considering NB and LR as individual classifiers and RF as an ensemble classifier to improve the predictive capabilities of the proposed model. In [4], [25], [40], [61], researchers employed a conditional probability based classifier called NB to analyze the impact of the proposed FS method. In [9], researchers employed SVM with its HI kernel to evaluate the effectiveness of the proposed model.…”

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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“…On the other hand, wrapper methods locate the most predictive feature subset with the use of search algorithms. It is expected that relevant feature subsets may produce a better prediction ability compared to the features alone [5]. In this study, we evaluate filtering based feature selection algorithms to obtain an effective feature subset.…”

Section: Introductionmentioning

confidence: 99%

Majority vote feature selection algorithm in software fault prediction

Borandağ¹,

Özçift²,

Kılınç³

et al. 2019

ComSIS

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Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance.

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Selecting best attributes for software defect prediction

Cited by 9 publications

References 16 publications

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

Analysis of Feature Selection Methods in Software Defect Prediction Models

Majority vote feature selection algorithm in software fault prediction

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