Software Defect Prediction Model Based on Stacked Denoising Auto-Encoder

Zhu, Yu; Yin, Dongjin; Gan, Yingtao; Rui, Lanlan; Xia, Guoxin

doi:10.1007/978-3-030-22971-9_2

Cited by 4 publications

(2 citation statements)

References 8 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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