Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature

Kumar, P. Suresh; Behera, Himansu Sekhar; Nayak, Janmenjoy; Naik, Bighnaraj

doi:10.1007/s11334-021-00399-2

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…Figs. [3][4][5][6][7][8][9][10][11] present the comparison on model loss without and with dropout layer on 9 datasets. For each figure, the graph on the left side is without a dropout layer while the one on the right side is with a dropout layer.…”

Section: Rq3: the Impact Of Using Different Structures On 1d-cnn Performancementioning

confidence: 99%

“…Figs. [3][4][5][6][7][8][9][10][11] show that a dropout layer can help in reducing the testing error and can make the model more fit. We can conclude that adding a dropout layer to a 1D-CNN classifier can reduce overfitting, hence improve the performance of the model in predicting software defects.…”

Section: Rq3: the Impact Of Using Different Structures On 1d-cnn Performancementioning

confidence: 99%

“…Hence, it helps the developer to find bugs in code. This procedure reduces the work in the maintenance phase and improves the quality of the software when deployed [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

Pin¹,

Chang²,

Nam³

2022

Computers, Materials &Amp; Continua

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Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1-Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the knowledge in the data sequence, reduce overfitting, and finally, predict whether the units of code are defects prone. We design large-scale empirical studies to reveal the proposed model's effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets. The experimental results demonstrate that in terms of f-measure, an optimal and modest 1D-CNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79% and 23.88%, respectively, in ways that minimize overfitting and improving prediction performance for software defects. According to the results, 1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering. This, in turn, could lead to saving software development resources and producing more reliable software.

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Section: Rq3: the Impact Of Using Different Structures On 1d-cnn Performancementioning

confidence: 99%

Section: Rq3: the Impact Of Using Different Structures On 1d-cnn Performancementioning

confidence: 99%

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Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

Pin¹,

Chang²,

Nam³

2022

Computers, Materials &Amp; Continua

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“…Second, Unsupervised Learning (UL), in which the results are unknown. Khurma et al [ 18 ] and Kumar et al [ 19 ] found that the most popular types of learning for SDP involve SL with binary classification, whereby the input from the module is classified by the output as either being defect-free or containing defects. Figure 3 presents the types of learning algorithms used in SDP with UL and SL.…”

Section: Introductionmentioning

confidence: 99%

A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

Mcmurray

Sodhro

2023

Sensors

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Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.

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“…Machine learning (ML)-based algorithms, such as Random Forest (RF), Bayesian and Logistic Regression (LR) are the most popular and fascinating in the recently published literature on SDP, and these approaches have been demonstrated to be beneficial for detecting defect-prone modules [18]. Regrettably, there is still a serious issue to be resolved: In nature, software defect datasets have a class imbalance, meaning that defective instances make up a tiny percentage of the data whereas the majority of software modules are defect-free.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction Based Ensemble Approach

Harikiran¹,

Chandana²,

Srinivasarao³

et al. 2023

Computer Systems Science and Engineering

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Software systems have grown significantly and in complexity. As a result of these qualities, preventing software faults is extremely difficult. Software defect prediction (SDP) can assist developers in finding potential bugs and reducing maintenance costs. When it comes to lowering software costs and assuring software quality, SDP plays a critical role in software development. As a result, automatically forecasting the number of errors in software modules is important, and it may assist developers in allocating limited resources more efficiently. Several methods for detecting and addressing such flaws at a low cost have been offered. These approaches, on the other hand, need to be significantly improved in terms of performance. Therefore in this paper, two deep learning (DL) models Multilayer preceptor (MLP) and deep neural network (DNN) are proposed. The proposed approaches combine the newly established Whale optimization algorithm (WOA) with the complementary Firefly algorithm (FA) to establish the emphasized metaheuristic search EMWS algorithm, which selects fewer but closely related representative features. To find the best-implemented classifier in terms of prediction achievement measurement factor, classifiers were applied to five PROMISE repository datasets. When compared to existing methods, the proposed technique for SDP outperforms, with 0.91% for the JM1 dataset, 0.98% accuracy for the KC2 dataset, 0.91% accuracy for the PC1 dataset, 0.93% accuracy for the MC2 dataset, and 0.92% accuracy for KC3.

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Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature

Cited by 31 publications

References 28 publications

Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

Software Defect Prediction Based Ensemble Approach

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