Topic cohesion preserving requirements clustering

Misra, Janardan; Sengupta, Shamik; Podder, Sanjay

doi:10.1145/2896995.2896998

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…There are diverse research efforts on crowd requirements engineering in the surveyed literature using AI techniques. For example, there are works on using natural language processing (NLP) techniques in classifying, clustering, categorizing users' feedback into feature requests, bugs, or simple compliments [16,37,41,53,69]. Analysis of user feedbacks and runtime human-computer interactions are experimented using NLP and text mining techniques.…”

Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering.

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Section: A Research Map For Intelligent Crowdrementioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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“…We recognized 5 papers presenting approaches to cluster requirements. These papers used the resultant clusters to understand the main functional groups or topics over requirements [76]- [78], to organize requirements in a tree structure (hierarchy) [79], or as a step towards discovering redundancy and inconsistency between requirements [80]. • Semantic Role Labeling: Semantic role labeling is the task of extracting semantic information from a software requirements specification [81].…”

Section: ) Requirements Analysismentioning

confidence: 99%

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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“…We recognized 5 papers presenting approaches to cluster requirements. These papers used the resultant clusters to understand the main functional groups or topics over requirements [76,77,78], to organize requirements in a tree structure (hierarchy) [79], or as a step towards discovering redundancy and inconsistency between requirements [80].…”

Section: Requirements Analysismentioning

confidence: 99%

“…Then, each requirement is represented as a vector representation in this k-dimensional space. In most related papers, this representation is employed to calculate the similarity between requirements as a part of similarity-based rules [70,107] or clustering machine learning techniques [135,99,78].…”

Section: Topic Modeling Based Representationsmentioning

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

Topic cohesion preserving requirements clustering

Cited by 10 publications

References 32 publications

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

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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