Feature selection in software defect prediction: A comparative study

Kakkar, Misha; Jain, Sarika

doi:10.1109/confluence.2016.7508200

Cited by 31 publications

(17 citation statements)

References 16 publications

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“…Pemilihan fitur yang digunakan adalah chisquared dan korelasi pada dataset defect software yang melibatkan lima dataset MDP NASA. Hasil penelitian hanya mendapatkan sedikit fitur dari hasil seleksi fitur, yaitu sekitar 80% dari total fitur yang tersedia [3]. Selain itu, penelitian [4]…”

Section: Study Literaturunclassified

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Metrics Based Feature Selection for Software Defect Prediction

Nugroho¹,

Abadi²,

Faisal³

et al. 2020

komputasi

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Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.

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Section: Study Literaturunclassified

“…Metode ini mengandung satu langkah penting, yang disebut pemilihan fitur. Langkah ini dapat meningkatkan kinerja klasifikasi, seperti yang dilakukan oleh banyak peneliti [3], [4], [5], dan [6].…”

Section: Introductionunclassified

Metrics Based Feature Selection for Software Defect Prediction

Nugroho¹,

Abadi²,

Faisal³

et al. 2020

komputasi

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“…One of the major drawbacks is that they don't predict defect density phase wise. Also, existing studies have concluded that metric selection plays a very important role in defect prediction [18] [19]. Thus metric selection is a very import step in SDP model building.…”

Section: A Metric Selectionmentioning

confidence: 99%

Fuzzy Logic Based Model to Predict Per Phase Software Defect

2019

IJITEE

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Software reliability is expressed as the probability of software to function properly under specified condition for a specified time period. A basic method to evaluate the software reliability is to check the presence of defects in the software. The presence of defect can be calculated as defect density measured defined as total number of defects present in the software divided by the size of the software. The paper proposes a fuzzy logic based model to predict per phase software defect density. The model uses 3 relevant software metrics per SDLC phase. Defect density prediction is a useful measure, which indicates the critical modules of the project and helps software teams to plan their resources in an efficient manner. The proposed model results are better in comparison with existing literature in the same domain when compared using MRE performance measure on 20 project dataset.

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“…In other words, FS methods select prominent features while ensuring the quality of the dataset. In the end, this solves the high dimensionality problem of software defect datasets [24,25].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Balogun

Basri

Capretz

et al. 2021

Entropy

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Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter–filter relationships in selecting optimal features for SDP processes.

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Feature selection in software defect prediction: A comparative study

Cited by 31 publications

References 16 publications

Metrics Based Feature Selection for Software Defect Prediction

Metrics Based Feature Selection for Software Defect Prediction

Fuzzy Logic Based Model to Predict Per Phase Software Defect

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

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