A Survey on Software Defect Prediction Using Deep Learning

Akimova, Elena N.; Bersenev, Alexander; Deikov, Artem A.; Kobylkin, K. S.; Konygin, A. V.; Mezentsev, Ilya P.; Misilov, Vladimir E.

doi:10.3390/math9111180

Cited by 54 publications

(26 citation statements)

References 53 publications

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“…The development of a software defect detection model with high accuracy has proven to be a herculean task. Over the past decade, several approaches have been proposed, but most of them have not been able to meet the standards in their accuracy of predicting and detecting the defects [17,18]. According to [19], the introduction of network computing technologies like cloud computing has provided users all around the world with an affordable and flexible network-based service provision scheme.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud-based bug tracking software defects analysis using deep learning

Tao

Zhou

et al. 2022

J Cloud Comp

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Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

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Section: Literature Reviewmentioning

confidence: 99%

Cloud-based bug tracking software defects analysis using deep learning

Tao

Zhou

et al. 2022

J Cloud Comp

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“…The development of a software defect detection model with high accuracy has proven to be a herculean task. Over the past decade, several approaches have been proposed, but most of them have not been able to meet the standards in their accuracy of predicting and detecting the defects [5,12]. According to [24]], the introduction of network computing technologies like cloud computing has provided users all around the world with an affordable and flexible network-based service provision scheme.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud-Based Bug Tracking Software DefectsAnalysis using Deep Learning

Tao

Zhou

et al. 2022

Preprint

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Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. Early detection of defect-prone components in software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Notwithstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

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“…In a similar study, Naseem, Khan [41] explored the performance tree-based classifiers for SDP. Also, Akimova, Bersenev [42] experimented with the performance of deep learning methods in SDP. Findings from these studies indicated that ML approaches can be utilized for detecting defects in SDP processes.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Balogun

Basri

Capretz

et al. 2021

Entropy

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Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter–filter relationships in selecting optimal features for SDP processes.

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A Survey on Software Defect Prediction Using Deep Learning

Cited by 54 publications

References 53 publications

Cloud-based bug tracking software defects analysis using deep learning

Cloud-based bug tracking software defects analysis using deep learning

Cloud-Based Bug Tracking Software DefectsAnalysis using Deep Learning

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

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