Mapping Bug Reports to Relevant Source Code Files Based on the Vector Space Model and Word Embedding

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

doi:10.1109/access.2019.2922686

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…BiLSTM with domain-specific embedding defined for clinical de-identification on COVID-19 Italian data gains a micro F1-score of 94.48% (Catelli et al 2020 ). The localization of software bugs using GloVe and the POS tagging methodology achieved a maximum average precision of 30.70% (Liu et al 2019 ). A single neural network model to jointly learn the task of POS and semantic annotation is proposed to enhance the performance of existing rule-based systems for the Welsh language.…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

“… Wang et al ( 2019 ) Public opinion orientation analysis for text in the Chinese language Business news corpus in Chinese language, Chinese Opinion Analysis Evaluation 2011 corpus Document orientation analysis approach Word2Vec (CBOW) The proposed model achieves an accuracy of 87.23% 11. Liu et al ( 2019 ) software bug localization by obtaining semantic similarity between bug reports and code file Project dataset: Eclipse, SWT, AsepectJ, Zxing Information retrieval approach Word2Vec(Skip-Gram), GloVe The GloVe + POS tagging model achieves average precision of 30.7% 12. Ezeani et al ( 2019 ) Embedding for the Welsh language Welsh Wikipedia articles NN approach fastText NN + fastText + POS + SEM achieves an accuracy of 99.23% for multi-task taggers 13.…”

Section: Appendix Amentioning

confidence: 99%

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Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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“…NLP was used to reduce manual efforts in remedying faults outlined in bug reports by automating redundant tasks such as reading and searching in natural language artifacts, and locating areas of concern. Examples include comparing bugs to generated patches (Csuvik et al, 2020), between bug reports and test cases (Gadelha et al, 2021), between bug reports and source code (Khatiwada et al, 2017;Liu et al, 2019;Wang et al, 2014;Lam et al, 2015;Malhotra et al, 2018;Jiang et al, 2020;Zhou et al, 2017;Gharibi et al, 2018;Shokripour et al, 2013), cross-language bug tracing (Xia et al, 2014), and commit information (Yang and Lee, 2021).…”

Section: Bug Localisationmentioning

confidence: 99%

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Pauzi

Capiluppi

2022

SSRN Journal

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“…At the same time, the precision of each file is calculated. Below, the relevant metrics are calculated according to the following example [36].…”

Section: Map (Mean Average Precision): the Metric Requiresmentioning

confidence: 99%

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

et al. 2019

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Software bug localization is very important in software engineering, but it is also complicated and time consuming. To improve the efficiency of developers, researchers have developed various traditional bug localization and machine learning bug localization methods. In this paper, we propose a novel method that improves bug localization performance. First, surface lexical correlation matching between bug reports and source code files is used to obtain features by deep neural network. Second, to solve the lexical gap between bug reports and source code files, semantic correlation matching between them is used to obtain features based on word embedding and sentence embedding by deep neural network. Then, the joint features obtained by the surface lexical and semantic correlation matching are fused into a unified feature representation for bug reports and source code files. In addition, since our experimental datasets are imbalanced data, we use a focal loss function to solve the impact of data imbalance. Finally, our method obtains the relatively high bug localization performance compared to other classic methods.

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Mapping Bug Reports to Relevant Source Code Files Based on the Vector Space Model and Word Embedding

Cited by 8 publications

References 26 publications

Impact of word embedding models on text analytics in deep learning environment: a review

Impact of word embedding models on text analytics in deep learning environment: a review

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

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

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