Crawling Wikipedia Pages to Train Word Embeddings Model for Software Engineering Domain

Mishra, Siba; Sharma, Arpit

doi:10.1145/3452383.3452401

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…One of the retrieved papers (Mishra et al [139]) focused on building a language model for the software requirement domain. This model was based on a domain-specific text corpus collected by crawling the software engineering category on Wikipedia.…”

Section: ) Othersmentioning

confidence: 99%

“…For example, the domain-specific meaning of words like "virus", "cookies", "Python", "fork", etc. can not be captured based on models trained on generic corpora [139]. Building such domainspecific word embedding models is notoriously a challenging task, especially with the little size of data available in the RE domain.…”

Section: ) More Re Domain-specific Word Embedding Modelsmentioning

confidence: 99%

“…Building such domainspecific word embedding models is notoriously a challenging task, especially with the little size of data available in the RE domain. One of the recent works [139] provided an embedding model trained on 92 MB of texts collected from Wikipedia pages related to the software engineering domain. However, more research is needed (1) to have embedding models trained on more practical industrial texts, (2) and to evaluate the use of these models in various RE tasks.…”

Section: ) More Re Domain-specific Word Embedding Modelsmentioning

confidence: 99%

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The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

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Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLPbased representations are usually used at a later stage as inputs for machine learning techniques or rulebased methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out (1) what are the representations used in RE tasks literature, (2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

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Section: ) Othersmentioning

confidence: 99%

Section: ) More Re Domain-specific Word Embedding Modelsmentioning

confidence: 99%

Section: ) More Re Domain-specific Word Embedding Modelsmentioning

confidence: 99%

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The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

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Section: More Re Domain-specific Word Embedding Modelsmentioning

confidence: 99%

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Sonbol,

Rebdawi,

Ghneim

2022

Preprint

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Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLP-based representations are usually used at a later stage as inputs for machine learning techniques or rule-based methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out ( 1) what are the representations used in RE tasks literature, ( 2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

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How are discussions linked? A link analysis study on GitHub Discussions

Lima,

Steinmacher,

Ford

et al. 2025

Journal of Systems and Software

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Crawling Wikipedia Pages to Train Word Embeddings Model for Software Engineering Domain

Cited by 6 publications

References 24 publications

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

How are discussions linked? A link analysis study on GitHub Discussions

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