SVM with Feature Selection and Extraction Techniques for Defect-Prone Software Module Prediction

Kumar, R. Ravi; Singh, Krishna Pratap

doi:10.1007/978-981-10-3325-4_28

Cited by 6 publications

(10 citation statements)

References 6 publications

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“…Patra and Bruzzone [20] have explored combined advantage of self-organizing map neural network and support vector machine to select uncertain and diverse samples in image classification. e effective combination of feature selection and feature extraction technique with SVM [21] is used for the prediction of defective software modules. e correlation-based feature selection technique with SVM has been compared with other available techniques to prove accuracy claimed in results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Umbrajkaar

Krishnamoorthy

Dhumale

2020

Shock and Vibration

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The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

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Section: Introductionmentioning

confidence: 99%

“…e SVM-based fault classification has been exercised in the domain of mechanical engineering [17][18][19][20][21][22][23]32]. e on-field application and use of ANN-SVM approach for various engineering domain has been explained [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Umbrajkaar

Krishnamoorthy

Dhumale

2020

Shock and Vibration

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“…For defect prediction, several machine learning algorithms are used, together with SVM [7], Bayesian Belief Network [22], NB [8], DT [5,] Neural Network [23], and Ensemble Learning [24]. For example, Kumar and Singh [26] evaluated the capabilities of SVMs in predicting defective code modules employing a combination of various feature choice and extraction techniques and tested them on 5 National Aeronautics and Space Administration (NASA) datasets. The author of [22] used the Thomas Bayesian belief network to predict software quality.…”

Section: B Deep Learning Based Software Defect Predictionmentioning

confidence: 99%

Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

2022

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The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance effort. Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code. Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems. In this paper, an effort has been made to categorize the Control Flow Graphs (CFGs) nodes according to their node features. In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs). We then incorporated CCMs' results as a node feature value in CFGs and generated the same based on the node connectivity for a graph. In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN). We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects. The Python programming language is used, along with Keras and TensorFlow. Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes. Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs. Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyperparameter tuning procedures are used to evaluate the approaches. The experimental results show that the proposed models outperformed state-of-the-art methods such as Nave Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) in all evaluation criteria.

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“…Meanwhile, many machine learning algorithms have been adopted for defect prediction, including Support Vector Machine (SVM) [24], Bayesian Belief Network [25], Naive Bayes (NB) [26], Decision Table (DT) [1], neural network [27], and ensemble learning [28]. For instance, Kumar and Singh [24] evaluated the capability of SVM with combinations of different feature selection and extraction techniques in predicting defective software modules and tested on five NASA datasets. In [25], the authors predicted the quality of a software by using the Bayesian Belief Network.…”

Section: Related Studiesmentioning

confidence: 99%

“…It can find the possible defective code blocks according to the features of historical data, thus allowing workers to focus their limited resources on the defect-prone code. Figure 2 presents a basic framework of software defect prediction and has been widely used in existing studies [1,8,12,18,19,24,25].…”

Section: Preliminariesmentioning

confidence: 99%

An Approach to Semantic and Structural Features Learning for Software Defect Prediction

Meilong

Xiao

et al. 2020

Mathematical Problems in Engineering

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Research on software defect prediction has achieved great success at modeling predictors. To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features. Few models, however, consider the semantic and structural features of programs. Understanding the context information of source code files could explain a lot about the cause of defects in software. In this paper, we leverage representation learning for semantic and structural features generation. Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features. Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN). After that, to learn the structural features, we apply the network embedding method to the resulting SN. Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S). We evaluated our method on 6 projects collected from public PROMISE repositories. The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better. In addition, compared with the traditional hand-crafted features, the F-measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%. We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.

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SVM with Feature Selection and Extraction Techniques for Defect-Prone Software Module Prediction

Cited by 6 publications

References 6 publications

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

An Approach to Semantic and Structural Features Learning for Software Defect Prediction

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