Software defect distribution prediction model based on NPE-SVM

Hua, Wei; Shan, Chun; Hu, Changzhen; Sun, Huizhong; Lei, Min

doi:10.1109/cc.2018.8387996

Cited by 15 publications

(10 citation statements)

References 10 publications

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“…The second question is regarding the machine learning techniques that are mostly used for building the model and NN has been chosen as the frequently used technique. Traditional SVM has been chosen as the least technique to be used for bug prediction model, unless we did some integrations with other algorithms (Wei et al, 2018). FS and EL has been chosen as the best methods for choosing the appropriate classifiers or metrics because of their tree-like structure (Kumar, 2018;Laradji et al, 2015;Jakhar and Rajnish, 2018;Ni et al, 2017;Kalsoom et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In the future, we can improve the selection of bug prediction techniques using machine learning because merely using better technique than before does not guarantee the improvement of performance. Still, there are some researchers focused on proposing hybrid frameworks (Erturk and Sezer, 2015;Arar and Ayan, 2015;Rhmann et al, 2020;Miholca et al, 2018;Abaei et al, 2015;Ryu et al, 2015), or improving the existing techniques (Pan et al, 2019;Zhou et al, 2019;Rathore and Kumar, 2017;Wei et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…SVM might perform below expectation since they required parameter optimization to get great performance. Because of this problem, (Wei et al, 2018) integrated traditional SVM with NPE algorithm to improve SVM performance. NPE algorithm can holds the vital problems of bug measurement in highdimensional and small case.…”

Section: Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Techniques for Software Bug Prediction: A Systematic Review

Saharudin¹,

Koh²,

Kew³

2020

Journal of Computer Science

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The goal of software bug prediction is to identify the software modules that will have the likelihood to get bugs by using some fundamental project resources before the real testing starts. Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase. There are many techniques and approaches that can be used to build the prediction models, such as machine learning. This technique is widely used nowadays because it can give accurate results and analysis. Therefore, we decided to perform a review of past literature on software bug prediction and machine learning so that we can understand better about the process of constructing the prediction model. Not only we want to see the machine learning techniques that past researchers used, we also assess the datasets, metrics and performance measures that are used during the development of the models. In this study, we have narrowed down to 31 main studies and six types of machine learning techniques have been identified. Two public datasets are found to be frequently used and object-oriented metrics are the highly chosen metrics for the prediction model. As for the performance measure, both graphical and numerical measures are often used to evaluate the performance of the models. From the results, we conclude that the machine learning technique can predict the bug, but there are not many applications in this area that exist nowadays. There are a few challenges in constructing the prediction model. Thus, more studies need to be carried out so that a well-formed result is obtained. We also provide a recommendation for future research based on the results we got from this study.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Machine Learning Techniques for Software Bug Prediction: A Systematic Review

Saharudin¹,

Koh²,

Kew³

2020

Journal of Computer Science

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“…Machine Learning algorithms were used for solving different types of problems in data classification [4,5,6,7]. Machine learning technology has grown so quickly in recent years and has been extensively used in many scientific fields [8,9,10]. ML algorithms are loosely divided into three groups, namely supervised learning, unsupervised learning and reinforcement learning, as seen in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1. Machine Learning and its Types [8] There are many good classification techniques in the literature including artificial neural networks, k-nearestneighbors classifier, decision trees, Bayesian classifier and Support Vector Machine (SVM) algorithm. From these techniques, SVM is one of the best-known techniques to optimize the expected solution [14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

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Fault Prediction for Software System in Industrial Internet: A Deep Learning Algorithm via Effective Dimension Reduction

Yang

Fang

et al. 2019

Communications in Computer and Information Science

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Software defect distribution prediction model based on NPE-SVM

Cited by 15 publications

References 10 publications

Machine Learning Techniques for Software Bug Prediction: A Systematic Review

Machine Learning Techniques for Software Bug Prediction: A Systematic Review

Machine Learning Applications based on SVM Classification A Review

Fault Prediction for Software System in Industrial Internet: A Deep Learning Algorithm via Effective Dimension Reduction

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