Convolutional Neural Networks-Based Locating Relevant Buggy Code Files for Bug Reports Affected by Data Imbalance

Liu, Guangliang; Shi, Ke; Chang, Jingfei; Wei, Xing

doi:10.1109/access.2019.2940557

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…Some of the exciting work done with CNN is as follows. Exploration in [ 49 ] and [ 50 ] aims at devising an improvised CNN-based approach to improve the bug localization task in software engineering. In [ 49 , 51 ], a bidirectional LSTM algorithm is proposed based on CNN and independent RNN for malicious web page identification.…”

Section: Bert Explorationmentioning

confidence: 99%

Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP

Althar

Samanta

Kaur

et al. 2021

Computational Intelligence and Neuroscience

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Security of the software system is a prime focus area for software development teams. This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. Various approaches in this context are explored using data of insurance domain-based software development. These approaches will facilitate an easy understanding of the practical challenges associated with actual-world implementation. This paper also discusses the capabilities of language modeling and its role in the knowledge system. The source code is modeled to build a deep software security analysis model. The proposed model can help software engineers build secure software by assessing the software security during software development time. Extensive experiments show that the proposed models can efficiently explore the software language modeling capabilities to classify software systems’ security vulnerabilities.

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Section: Bert Explorationmentioning

confidence: 99%

Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP

Althar

Samanta

Kaur

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…Moreover, efforts have been made to address bug reports imbalance for bug localization using cross project features (Huo et al, 2019) and Focal Loss function (Liu et al, 2019).…”

Section: Bug Localizationmentioning

confidence: 99%

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Ahmad

Kholief

et al. 2023

Preprint

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Many software projects utilize Bug Tracking System (BTS) to manage and process bug reports. Over the years, the number of bug report submissions has increased exponentially with some projects receiving as many as about a hundred submissions daily. Bug report processing (BRP) consists of five key processes: duplicate detection, severity prediction, fix-time prediction, bug triage, and bug localization. Previously, traditional machine learning (ML) algorithms -based models were proposed to automate BRP tasks. However, in recent years deep BRP (DBRP) models were proposed to exploit the ever-increasing bug repositories for automatic extraction of semantic and contextual features of bug reports. Although, some papers reviewed related literature from many perspectives of software maintenance, no existing work comprehensively reviewed the state of DBRP. In this paper, we review the state of DBRP models in the five key BRP tasks. For this, we collect papers from four international databases published between 2015 and 2021. We evaluate the papers with regard to the deep neural networks, text representation models, and bug report features utilized. Finally, we present findings and prospects for future research works in DBRP. Our review analyzes how word embedding, CNNs, LSTMs, attention mechanism are used to represent bug report and source code, model performance and challenges such as data imbalance, feature utilization and complexity.

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“…Unfortunately, reducing the high debugging costs and the number of software defects is a challenging problem, especially considering the limited testing resources of software team [3], [4], and often facing strong pressure for rapid delivery [3], [5]. Therefore, researchers have introduced machine learning methods to predict defects in software source code [6], such as Naive Bayes (NB) [7], [8], support vector machine (SVM) [7], decision trees [8], and neural networks [9]. Malhotra et al have been proposed for software defect detection based on the measure of internal metrics and defect data from similar projects or earlier releases to construct defect detection models [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Imbalanced Ensemble Learning in Software Defect Predication

et al. 2021

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With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the characteristics of imbalanced distribution of defect data sets have led to the deviation and loss of accuracy of most software defect prediction methods. This paper presents two novel approaches for learning from imbalanced data sets to produce a higher predictive accuracy over the minority class. These two methods differ in whether the oversampling and the misclassification cost information are utilized during training stage, and they are good at different aspects of imbalanced classification, one for dealing with highly imbalanced data sets and the other for moderately imbalanced data sets. Comparing with other state-of-the-art imbalance learning algorithms on imbalanced datasets, the experimental results show that these two methods have achieved excellent results in terms of G-mean and AUC measures, and more accurately identified the defective modules to reduce the cost of detection system.

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Convolutional Neural Networks-Based Locating Relevant Buggy Code Files for Bug Reports Affected by Data Imbalance

Cited by 12 publications

References 28 publications

Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP

Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

A Novel Imbalanced Ensemble Learning in Software Defect Predication

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