Experimental study on feature selection methods for software fault detection

Singh, Devender; Fernando, Anil; Leavline, E. Jebamalar

doi:10.1109/iccpct.2016.7530156

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Forward Selection [31] works similarly to Backward elimination but in an opposite direction. First step we have a model with the only response variable.…”

Section: Marco Canaparomentioning

confidence: 99%

Software defect prediction: A study on software metrics using statistical and machine learning methods

Canaparo¹,

Ronchieri²,

Bertaccini³

2022

Proceedings of International Symposium on Grids &Amp; Clouds 2022 — PoS(ISGC2022)

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Software defect prediction aims at identifying defect prone software modules in order to allocate optimal testing resources. The role of testing in software development life cycle is vital especially when software systems are becoming more and more complex, representing a suitable environment for defects. Several researchers have striven to develop models able to determine defective modules with the aim of reducing time and cost of software testing. Such models are typically trained on software measurements, known as software metrics, which describe the characteristics of a software project in terms of e.g., dimension and complexity. These metrics reduce the subjectivity of software quality assessment and can be relied on for decision making, e.g., to decide where to focus software tests. The aim of our work is to employ both feature selection or construction techniques and machine learning techniques to build software defect prediction models on different kinds of software dataset metrics (derived from various software projects available in the NASA and Eclipse repositories), and assess their performances by considering accuracy, precision, recall and area under the curve. We have used non parametric tests to compute the statistical significance of the obtained results. The collected metrics belong to three main categories: dimension, complexity and object orientation. The involved datasets contain class labels, i.e., information on the defectiveness of the software modules. To make our study available to research community, we have developed an open source and extensible R application that supports researchers to load the selected kinds of datasets, to filter them according to the their features and to apply machine learning techniques.

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“…Forward Selection [31] works similarly to Backward elimination but in an opposite direction. First step we have a model with the only response variable.…”

Section: Marco Canaparomentioning

confidence: 99%

Software defect prediction: A study on software metrics using statistical and machine learning methods

Canaparo¹,

Ronchieri²,

Bertaccini³

2022

Proceedings of International Symposium on Grids &Amp; Clouds 2022 — PoS(ISGC2022)

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“…In previous research [5], filtering feature selection methods were used to improve NB result in predicting software fault using Turkish white-goods manufacturer datasets. Filtering feature selection methods used in the previous research were Gain Ratio (GR), Information Gain (IG), and One-R (OR).…”

Section: Fachrul Pralienka Bani Muhamad Is With Department Of Informamentioning

confidence: 99%

Software Fault Prediction Using Filtering Feature Selection in Cluster-Based Classification

Muhamad¹,

Siahaan

Fatichah

2018

IJPS

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Abstract-The high accuracy of software fault prediction can help testing effort and improving software quality. Previous researchers had proposed the combination of Entropy-Based Discretization (EBD) and Cluster-Based Classification (CBC). However, the irrelevant and redundant features in software fault dataset tend to decrease the prediction accuracy value. This study proposes improvement of CBC outcomes by integrating filtering feature selection methods. Filtering feature selection methods that will be integrated with CBC i.e. Information Gain (IG), Gain Ratio (GR), and One-R (OR). Based on the research using 2 datasets NASA public MDP (i.e. PC2 and PC3), the result shows that the combination of CBC and IG yields the best average accuracy value compared to GR and OR. It generates 67.52% average of probability detection (pd) and 37.42% average of probability false alarm (pf). While CBC without feature selection yields 65.38% average pd and 49.95% average pf. It can be concluded that IG can improve CBC outcomes by increasing 2.14% average pd and reducing 12.53% average pf.

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“…Dikarenakan kemiripan rumus SU terhadap IG dan GR, maka hasil dapat menunjukkan nilai yang tidak jauh berbeda [5]. Namun pada penelitian yang lain, hasil perhitungan SU berbeda dengan hasil perhitungan IG dan GR [7].…”

Section: E Symmetric Uncertainty (Su)unclassified

“…SELEKSI FITUR Berdasarkan penelitian sebelumnya, penggunaan semua fitur pada data set sebagai masukan model prediksi belum tentu dapat meningkatkan nilai prediksi [4], [5]. Salah satu faktor penyebabnya adalah kualitas data dari setiap fitur.…”

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Perbaikan Prediksi Kesalahan Perangkat Lunak Menggunakan Seleksi Fitur dan Cluster-Based Classification

Muhamad¹,

Siahaan²,

Fatichah³

2017

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-High balance value of software fault prediction can help in conducting test effort, saving test costs, saving test resources, and improving software quality. Balance values in software fault prediction need to be considered, as in most cases, the class distribution of true and false in the software fault data set tends to be unbalanced. The balance value is obtained from trade-off between probability detection (pd) and probability false alarm (pf). Previous researchers had proposed Cluster-Based Classification (CBC) method which was integrated with EntropyBased Discretization (EBD). However, predictive models with irrelevant and redundant features in data sets can decrease balance value. This study proposes improvement of software fault prediction outcomes on CBC by integrating feature selection methods. Some feature selection methods are integrated with CBC, i.e. Information Gain (IG), Gain Ration (GR), One-R (OR), Relief-F (RFF), and Symmetric Uncertainty (SU). The result shows that combination of CBC with IG gives best average balance value, compared to other feature selection methods used in this research. Using five NASA public MDP data sets, the combination of IG and CBC generates 63.91% average of balance, while CBC method without feature selection produce 54.79% average of balance. It shows that IG can increase CBC balance average by 9.12%.Intisari-Tingginya nilai balance pada prediksi kesalahan perangkat lunak dapat membantu usaha pengujian, menghemat biaya pengujian, menghemat sumber daya pengujian, dan meningkatkan kualitas perangkat lunak. Nilai balance pada hasil prediksi kesalahan perangkat lunak perlu menjadi perhatian, karena pada umumnya, persebaran kelas true dan kelas false pada data set kesalahan perangkat lunak cenderung tidak seimbang. Nilai balance diperoleh dari hasil tarik-ulur (trade-off) antara nilai probability detection (pd) dan probability false alarm (pf). Peneliti sebelumnya mengusulkan metode Cluster-Based Classification (CBC) yang diintegrasikan dengan Entropy-Based Discretization (EBD). Namun, model prediksi dengan fitur yang redundan dan tidak relevan pada data set dapat menurunkan nilai balance. Dalam makalah ini diusulkan peningkatan hasil prediksi kesalahan perangkat lunak pada metode CBC dengan mengintegrasikan metode seleksi fitur. Adapun metode seleksi fitur akan diintegrasikan dengan CBC yaitu Perolehan Informasi (IG), Rasio Perolehan (GR), One-R (OR), Relief-F (RFF), dan Symmetric Uncertainty (SU). Hasil menunjukkan bahwa kombinasi CBC dengan IG menghasilkan nilai rata-rata balance terbaik, dibandingkan dengan keempat metode seleksi fitur lainnya. Kombinasi metode IG dan CBC menghasilkan 63,91% nilai rata-rata balance, sedangkan metode CBC tanpa seleksi fitur menghasilkan 54,79% nilai rata-rata balance. Jadi, IG dapat meningkatkan nilai rata-rata balance CBC sebesar 9,12%.Kata Kunci-Cluster-based Classification, Entropy-based Discretization, kesalahan perangkat lunak, seleksi fitur.I. PENDAHULUAN Prediksi kesalahan perangkat lunak sangat penting untuk dilakuka...

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Experimental study on feature selection methods for software fault detection

Cited by 6 publications

References 29 publications

Software defect prediction: A study on software metrics using statistical and machine learning methods

Software defect prediction: A study on software metrics using statistical and machine learning methods

Software Fault Prediction Using Filtering Feature Selection in Cluster-Based Classification

Perbaikan Prediksi Kesalahan Perangkat Lunak Menggunakan Seleksi Fitur dan Cluster-Based Classification

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