Automating orthogonal defect classification using machine learning algorithms

Lopes, Fábio; Agnelo, João; Teixeira, César; Laranjeiro, Nuno; Bernardino, Jorge

doi:10.1016/j.future.2019.09.009

Cited by 38 publications

(10 citation statements)

References 32 publications

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“…However, handcrafted features utilizing prior knowledge cannot effectively guarantee the validity and generalization of the model. According to [5], traditional machine learning methods have insufficient generalization for defect detection.…”

Section: Related Workmentioning

confidence: 99%

Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure

Zhang¹,

Wei

Qi³

et al. 2022

J. Phys.: Conf. Ser.

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When detecting aircraft flared tube defect with the YOLOv4 network structure, tiny object defects will be missed, and resulting in a high missed detection rate and low mAP (mean Average Precision) value. This paper proposes an improved aircraft flared tube defect detection algorithm of YOLOv4 network structure. Firstly, in order to improve the feature extraction capability of the YOLOv4 network for tiny defects, a convolution operation is added to the SPP (Spatial Pyramid Pooling) and PANet (Path Aggregation Network) structure. Secondly, the representation of the feature pyramid is enhanced utilizing the improved PANet. Thirdly, the decoupled head is utilized to improve the model performance. Finally, we construct the aircraft flared tube dataset by labeling the defect samples, and experiment with the improved YOLOv4 network. Experimental results show that the mAP value of defect detection task is increased from 91.26% to 95.31%, average detection time increased from 346.17ms to 278.61ms.

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Section: Related Workmentioning

confidence: 99%

Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure

Zhang¹,

Wei

Qi³

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…Lopes et al [43] used ODC for classifying software defects, indicating that it requires one or more experts to categorize each defect in a reasonably complex and time-consuming process. They evaluated the applicability of a set of machine learning algorithms (NB, KNN, SVM, Recurrent Neural Networks (RNN), Nearest Centroid, and RF) for performing automatic classification of software defects based on ODC and using unstructured text bug reports.…”

Section: Automatic Bug Classification With Machine Learning Algorithmsmentioning

confidence: 99%

Automated Classification of Unstructured Bilingual Software Bug Reports: An Industrial Case Study Research

Köksal

Tekinerdoğan

2021

Applied Sciences

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Software bug report classification is a critical process to understand the nature, implications, and causes of software failures. Furthermore, classification enables a fast and appropriate reaction to software bugs. However, for large-scale projects, one must deal with a broad set of bugs from multiple types. In this context, manually classifying bugs becomes cumbersome and time-consuming. Although several studies have addressed automated bug classification using machine learning techniques, they have mainly focused on academic case studies, open-source software, and unilingual text input. This paper presents our automated bug classification approach applied and validated in an industrial case study. In contrast to earlier studies, our study is applied to a commercial software system based on unstructured bilingual bug reports written in English and Turkish. The presented approach adopts and integrates machine learning (ML), text mining, and natural language processing (NLP) techniques to support the classification of software bugs. The approach has been applied within an industrial case study. Compared to manual classification, our results show that bug classification can be automated and even performs better than manual bug classification. Our study shows that the presented approach and the corresponding tools effectively reduce the manual classification time and effort.

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“…To calculate the classification accuracy of classifiers, the accuracy metric has been primarily used in studies [73], [74]. The accuracy metric involves the confusion matrix measures, as shown in the following Eq.…”

Section: Trust Predictionmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

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To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

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Automating orthogonal defect classification using machine learning algorithms

Cited by 38 publications

References 32 publications

Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure

Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure

Automated Classification of Unstructured Bilingual Software Bug Reports: An Industrial Case Study Research

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

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