NLP4IP: Natural Language Processing-based Recommendation Approach for Issues Prioritization

Shafiq, Saad; Mashkoor, Atif; Mayr-Dorn, Christoph; Egyed, Alexander

doi:10.1109/seaa53835.2021.00022

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…• Requirements Prioritization: the main target for this task is to determine which candidate requirements of a software product should be included in a certain release. We recognized 5 papers handling this task [132]- [136]. • Effort Estimation: This task focuses on estimating the effort involved in implementing a requirement [137].…”

Section: ) Requirements Managementmentioning

confidence: 99%

“…• Requirements Management: Most of the papers under this category use lexical and syntactic features representation [133], [134], [136]. However, the number of NLP-based publications related to this category is still limited to have a clear conclusion regarding the suitable representations for this type of tasks.…”

Section: ) Research Directions For Each Category Of Re Tasksmentioning

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: ) Requirements Managementmentioning

confidence: 99%

Section: ) Research Directions For Each Category Of Re Tasksmentioning

confidence: 99%

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

2022

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“…For the planning stage, AI can help find the most critical issues that need to be fixed on a priority basis 11 and help agile project management, including identifying and refining backlog items for sprint planning and managing risks. 4 For the requirements stage, AI can assist with recommendation approaches for task allocation.…”

Section: A Rtificial Intelligence (Ai)mentioning

confidence: 99%

Artificial Intelligence and Software Engineering: Are We Ready?

2022

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Section: Requirements Managementmentioning

confidence: 99%

“…• Requirements Management: Most of the papers under this category use lexical and syntactic features representation [136,133,134]. However, the number of NLP-based publications related to this category is still limited to have a clear conclusion regarding the suitable representations for this type of tasks.…”

Section: Research Directions For Each Category Of Re Tasksmentioning

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.

show abstract

NLP4IP: Natural Language Processing-based Recommendation Approach for Issues Prioritization

Cited by 9 publications

References 27 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

Artificial Intelligence and Software Engineering: Are We Ready?

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

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