Issue Link Label Recovery and Prediction for Open Source Software

Nicholson, Alexander; Jin, Libo

doi:10.1109/rew53955.2021.00024

Cited by 11 publications

(11 citation statements)

References 38 publications

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“…Researchers have discussed the lack of integration between revision control systems and issue tracking systems, which affects the recovery of trace links, as well as the prediction of software faults [16], [51], [52]. Researchers addressed that issues form a complex network by themselves, but there were no approaches to predict link types [40], [43].…”

Section: ) Informationmentioning

confidence: 99%

A Systematic Literature Review of Issue-Based Requirement Traceability

et al. 2023

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Issue reports are software artifacts that often specify the changed requirements of software systems. As software systems evolve according to these changed requirements, issue reports have become the essential artifacts that should be covered by requirement traceability. While researchers have developed automatic approaches for establishing the traceability links of issue reports, no papers have surveyed these approaches. In this paper, we conduct a systematic literature review of issue-based requirement traceability. We searched for articles published in renowned conferences and journals in the software engineering field from 2011 to 2022. From 1,347 initial articles, we identified 40 relevant articles. We investigated four aspects of issue-based traceability: problems, artifact pairs, techniques, and evaluation targets. Our findings are as follows. First, the challenges of issue-based requirement traceability are relevant to accuracy, effort, support, information, and trustworthiness. Second, issue reports are linked to commits, source code, user reviews, and test cases. Third, the studies mainly adopted machine learning and information retrieval techniques to generate and recover trace links. Finally, the main evaluation targets were open-source projects, but open datasets were also provided.

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Section: ) Informationmentioning

confidence: 99%

A Systematic Literature Review of Issue-Based Requirement Traceability

et al. 2023

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“…• Requirements Traceability: Requirements traceability is used to capture the relationships between the requirements, the design, and the implementation of a system [55]. We recognized 21 papers related to this task, part of which focused on detecting the relationships between requirements (inter-dependency between requirements) [56]- [66], while the remaining focused on detecting the relationships between requirements and other artifacts (design documents and source code) [55], [67]- [74]. • Requirements Clustering: Requirements clustering is used to organize software requirements into a set of clusters with high cohesion and low coupling [75].…”

Section: ) Requirements Analysismentioning

confidence: 99%

“…Aggregation-based [11], [47], [56], [58], [59], [64], [66], [76], [84], [91], [98], [101], [104], [128] RNN-based [43], [51], [54], [67], [112], [137] CNN-based [39], [50] TABLE 3. the used statement embedding techniques with their related paper…”

Section: Related Papersmentioning

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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“…• Requirements Traceability: Requirements traceability is used to capture the relationships between the requirements, the design, and the implementation of a system [55]. We recognized 21 papers related to this task, part of which focused on detecting the relationships between requirements (inter-dependency between requirements) [56,57,58,59,60,61,62,63,64,65,66], while the remaining focused on detecting the relationships between requirements and other artifacts (design documents and source code) [67,55,68,69,70,71,72,73,74].…”

Section: Requirements Analysismentioning

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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Issue Link Label Recovery and Prediction for Open Source Software

Cited by 11 publications

References 38 publications

A Systematic Literature Review of Issue-Based Requirement Traceability

A Systematic Literature Review of Issue-Based Requirement Traceability

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

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