A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

Yang, Cheng; Wu, Lingang; Yu, Chunyang; Zhou, Yuliang

doi:10.3390/info12050177

Cited by 5 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 2) Representing each requirement as a pre-defined set of linguistic features. (3) Then, proceeding to the solving phase which uses ML techniques (such as Decision Trees [52], [81], [97], [118], [138], SVM [10], [53], [88], and RF [49], [60], [90], [96]), or rule-based approach using a set of syntactic regular expressions [95], [117], [125], [126], [130].…”

Section: ) Lexical and Syntactic Featuresmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Section: ) Lexical and Syntactic Featuresmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

show abstract

“…More recently, Zhang and others [58] studied user reviews and formulate software feature prioritization as an optimization problem that targets maximizing app ratings for a certain user group. Yang et al [56] use a semi-automated approach to prioritize bugs to fix, they study 6 apps and extract fourteen features to classify reviews.…”

Section: Related Workmentioning

confidence: 99%

Proactive Prioritization of App Issues via Contrastive Learning

Moghis

Mosharrof

Farooq

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to proactively address issues in their apps. However, only a small number of reviews capture common issues and sentiments which creates a need for automatically identifying prominent reviews. Unfortunately, most existing work in text ranking and popularity prediction focuses on social contexts where other signals are available, which renders such works ineffective in the context of app reviews. In this work, we propose a new framework, PPrior, that enables proactive prioritization of app issues through identifying prominent reviews (ones predicted to receive a large number of votes in a given time window). Predicting highlyvoted reviews is challenging given that, unlike social posts, social network features of users are not available. Moreover, there is an issue of class imbalance, since a large number of user reviews receive little to no votes. PPrior employs a pre-trained T5 model and works in three phases. Phase one adapts the pretrained T5 model to the user reviews data in a self-supervised fashion. In phase two, we leverage contrastive training to learn a generic and task-independent representation of user reviews. Phase three uses radius neighbors classifier to make the final predictions. This phase also uses FAISS index for scalability and efficient search. To conduct extensive experiments, we acquired a large dataset of over 2.1 million user reviews from Google Play. Our experimental results demonstrate the effectiveness of the proposed framework when compared against several state-of-theart approaches. Moreover, the accuracy of PPrior in predicting prominent reviews is comparable to that of experienced app developers.

show abstract

“…• Extracting Requirements from Reviews and Forums: 9 papers [84,85,86,87,88,63,89,90,91] proposed solutions for this task, where various applications reviews and forums are used as a source for input texts such as App Store and Google Play.…”

Section: Requirements Extractionmentioning

confidence: 99%

“…(2) Representing each requirement as a pre-defined set of linguistic features. (3) Then, proceeding to the solving phase which uses ML techniques (such as Decision Trees [97,138,81,118,52], SVM [53,10,88], and RF [96,60,90,49]), or rule-based approach using a set of syntactic regular expressions [125,95,126,130,117]. The used pre-defined set of features (in step 2) usually includes features related to the following four groups:…”

Section: Lexical and Syntactic Featuresmentioning

confidence: 99%

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

Sonbol,

Rebdawi,

Ghneim

2022

Preprint

View full text Add to dashboard Cite

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

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

Cited by 5 publications

References 45 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

Proactive Prioritization of App Issues via Contrastive Learning

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

Contact Info

Product

Resources

About