Bug Localization for Version Issues With Defect Patterns

Sun, Xiaobing; Zhou, Wei; Li, Bin; Ni, Zhen; Lu, Jinting

doi:10.1109/access.2019.2894976

Cited by 20 publications

(5 citation statements)

References 41 publications

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“…Software parameters and metrics are essential to measure and assess various aspects of software development to ensure its quality and effectiveness [11]. Metrics are indicators that describe specific attributes of the software Process Efficiency, Effort Assessment, Defect Reduction and Project Performance Evaluation [21,22].…”

Section: Software Defects Metricsmentioning

confidence: 99%

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

2024

IJIES

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Software has become an essential and important part of every domain system. Developing quality software is critical to maintaining a stable and secure system. Most of the existing software defect prediction tasks focus on the various kinds of defects leftover in the software system, but they do not focus on the most common and frequent software defects that developers most commonly do and which have a significant effect on the quality of software development. These unnoticeable defects have a considerable impact on the functionality and also on development time, effort, and cost of the software. This paper propose a frequent software defects prediction (FSDP) mechanism based on defects Correlation learning method (CLM) utilizing various defects metrics. The aim of this work is to assist developers in accurately identifying software defects and support project managers in ensuring software quality by minimizing the presence of defect-prone code during development. The evaluation of FSDP was performed using NASA datasets in comparison with the conventional Naive Bayes, Support Vector Machine, and Random Forest classifiers and also compare with the state-of-the-arts methods to measure accuracy, precision, recall and F-Score to understand the impact of the prediction accuracy. The proposed FSDP achieves 98.88% accuracy and 98.86% precision in compare to state-of-the-arts classifiers methods indicates the effectiveness of the proposed approach in defect prediction.

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Section: Software Defects Metricsmentioning

confidence: 99%

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

2024

IJIES

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“…IR‐based bug localization. There are a lot of research studies applying information retrieval technology to bug localization [8–18, 36, 37, 39, 48, 53, 54]. Lukins et al.…”

Section: Related Workmentioning

confidence: 99%

“…Fine grained bug localization. In recent years, researchers have applied information retrieval technology to bug localization [8][9][10][11][12][13][14][15][16][17][18]. They use bug reports as queries, search for relevant code fragments, and rank the code fragments according to the similarity between the code fragments and the bug report.…”

Section: Introductionmentioning

confidence: 99%

An approach of method‐level bug localization

et al. 2022

IET Software

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Bug localization is an important field in software engineering research. The traditional bug localization approaches based on information retrieval separate words through lexical analysis. In this way, the comments of the source code are ignored or treated as plain text, which will lose some semantic information. In this paper, MBL_SHL, an automatic Method-level Bug Localization approach, which utilises code Summarization, Historical fixed bugs and code Length, is presented. Based on the code summarization technology, this approach first supplements the comment for uncommented code, and then calculates the Word2vec vector and Term Frequency-Inverse Document Frequency vector for the bug report, methods and comments, respectively. After that the authors calculate separately the similarity between the bug report and each method, the bug report and each comment. The code length information and historical fix information are also considered as a weight and a part of the score, respectively, to calculate the final score of each method. Finally, the scores are sorted to determine the list of methods that may need to be modified when fixing the software bugs. We built a method-granular bug localization dataset, which contains five open-source projects. The experimental results show that the proposed approach significantly outperforms the existing approaches on the method level.

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“…A new strategy that utilizes edition-related defect characteristics to pinpoint variant defects combined with the data extraction technique has been proposed to fill this gap [2]. In [3], it has been proposed to use stacked denoising autoencoders, a well-known machine learning paradigm, to generate deep representations from standard software measurements.…”

Section: Introductionmentioning

confidence: 99%

Software Fault Detection using Multi-Distinguished-Features Sampling with Ensemble Random Forest Classifier

2022

IJIES

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Finding faults in software modules is an emerging issue in software reliability systems, and the assessment of the fault is performed by software fault prediction systems (SFPS). The identification process of fault-prone software modules is one of the prioritized aspects before initiating the testing process of the same modules. The SFPS helps improve software quality within the specified time and cost values. Early fault prediction in SFPS for the different software components showed significant results concerning the cost and time parameters. According to the state-ofthe-art SFPS, ensemble-based classifiers were performed best and most cost-effective compared to other classifier methods. Recently, a random ensemble forest with adaptive synthetic sampling (E-RF-ADASYN) has been developed, is tested on a sample of PROMISE datasets, and shown the cost-effective classifier results. In the logistic regression to software quality models, and the other knowledge of account for prior probability and costs of misclassification. Probabilities and costs of misclassification in a logistic regression-based classification algorithm for software quality modeling. The decision tree algorithm is an ensemble learning approach for prediction. The algorithm works based on developing several decision trees and later decides the output class based on the most popular one. The proposed work focuses on developing an alternative sampling method called ensemble-random forest with multi-distinguishedfeatures sampling (E-RF-MDFS), for obtaining the best sample illustration for representing the entire dataset. Batinduced butterfly optimization (BBO) has been used for the feature extraction process. The experiments are conducted on 8 datasets of the PROMISE database. The proposed E-RF-MDFS has improved performance than E-RF-ADASYN in fault detection accuracy, real positive rate, and Pearson's correlation coefficient. On comparing the performance of E-RF-based classifiers, the performance of the proposed MDFS is the best, with an FDA of 99.3 % (Xalan v2.6) than the ADASYN classifier.

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Bug Localization for Version Issues With Defect Patterns

Cited by 20 publications

References 41 publications

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

An approach of method‐level bug localization

Software Fault Detection using Multi-Distinguished-Features Sampling with Ensemble Random Forest Classifier

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