Software Defect Prediction via Deep Belief Network

Hua, Wei; Shan, Chun; Hu, Changzhen; Zhang, Yu; Yu, Xiao

doi:10.1049/cje.2019.06.012

Cited by 16 publications

(12 citation statements)

References 33 publications

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“…To answer the second research question, we compare the performance of the proposed model with four state-of-the-art deep learning models: Defect Prediction with Deep Forest (DPDF) [53], Genetic Algorithm-Deep Neural Network (GA-DNN) [54], Deep Belief Network Prediction Model (DBNPM) [55], and Stack Denoising Auto-Encoder (SDAE) [56] and present the results in Tabs. 9 and 10.…”

Section: Rq2: the Performance Of The Proposed Model Compared To The State-of-the-artmentioning

confidence: 99%

“…Therefore, the experimental results might not be generalizable to other datasets, which might produce better or worse results for each software defect prediction model used in this study. However, the dataset we opted for is often used in previous software defect detection [53][54][55][56]. Different results can be generated by using different sets of software metrics.…”

Section: Threats To External Validitymentioning

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: Rq2: the Performance Of The Proposed Model Compared To The State-of-the-artmentioning

confidence: 99%

Section: Threats To External Validitymentioning

confidence: 99%

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

Pin¹,

Chang²,

Nam³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A number of well-known machine learning algorithms, such as Decision Trees, Artificial Neural Network (ANN), K nearest neighbor, SVM and Ensemble Learning have been considered, where the Stacking Ensemble technique proved to be the best with the best result for all data sets with an accuracy of defects prediction greater than 0.9. The study [3] proposes the Deep belief network prediction model (DBNPM), a system for determining whether a software module contains defects. The key idea of DBNPM is the Deep belief network (DBN) technology, which is an effective technique for deep learning in image and natural language processing, the characteristics of which are similar to the defects of the original program.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards a software defect proneness model: feature selection

Yakovyna¹,

Symets

2021

AAIT

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This article is focused on improving static models of software reliability based on using machine learning methods to select the software code metrics that most strongly affect its reliability. The study used a merged dataset from the PROMISE Software Engineering repository, which contained data on testing software modules of five programs and twenty-one code metrics. For the prepared sampling, the most important features that affect the quality of software code have been selected using the following methods of feature selection: Boruta, Stepwise selection, Exhaustive Feature Selection, Random Forest Importance, LightGBM Importance, Genetic Algorithms, Principal Component Analysis, Xverse python. Basing on the voting on the results of the work of the methods of feature selection, a static (deterministic) model of software reliability has been built, which establishes the relationship between the probability of a defect in the software module and the metrics of its code. It has been shown that this model includes such code metrics as branch count of a program, McCabe’s lines of code and cyclomatic complexity, Halstead’s total number of operators and operands, intelligence, volume, and effort value. A comparison of the effectiveness of different methods of feature selection has been put into practice, in particular, a study of the effect of the method of feature selection on the accuracy of classification using the following classifiers: Random Forest, Support Vector Machine, k-Nearest Neighbors, Decision Tree classifier, AdaBoost classifier, Gradient Boosting for classification. It has been shown that the use of any method of feature selection increases the accuracy of classification by at least ten percent compared to the original dataset, which confirms the importance of this procedure for predicting software defects based on metric datasets that contain a significant number of highly correlated software code metrics. It has been found that the best accuracy of the forecast for most classifiers was reached using a set of features obtained from the proposed static model of software reliability. In addition, it has been shown that it is also possible to use separate methods, such as Autoencoder, Exhaustive Feature Selection and Principal Component Analysis with an insignificant loss of classification and prediction accuracy

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“…This is because with the increasing complexity and importance of software systems across several fields, even the smallest malfunction or failure could pose a severe threat to the health and safety of users [4,5] . This has drawn considerable research attention to the problem of "software trustworthiness," which reflects the degree to which a software system can maintain stability and continue to perform in the expected manner when dis-turbed by uncertainty factors in a dynamically changing environment [6] . The existing research concerning software trustworthiness can be classified into software trustworthiness measurement, evaluation, and allocation [7] .…”

Section: Introductionmentioning

confidence: 99%

“…This has drawn considerable research attention to the problem of “software trustworthiness,” which reflects the degree to which a software system can maintain stability and continue to perform in the expected manner when disturbed by uncertainty factors in a dynamically changing environment [ 6 ] . The existing research concerning software trustworthiness can be classified into software trustworthiness measurement, evaluation, and allocation [ 7 ] . Software trustworthiness measurement and evaluation, which corresponds to the quantitative assessment of software systems to provide guidance on subsequent design and development, is significant for improving software quality [ 8 ] .…”

Section: Introductionmentioning

confidence: 99%