Identifying Key Features from App User Reviews

Wu, Huayao; Deng, Wenjun; Niu, Xintao; Nie, Changhai

doi:10.1109/icse43902.2021.00088

Cited by 26 publications

(20 citation statements)

References 38 publications

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“…• 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%

“…This part represents the second largest group of papers (24 papers) with a major increase in the last couple of years. It is noted that papers that use this kind of representations achieve promising results in many major tasks such as requirement classification [11,50,51], traceability [64,56,59,58], ambiguity detection [106], and requirement extraction [98,91].…”

Section: Advanced Embedding Representationsmentioning

confidence: 99%

“…-For BERT-based word embedding, a special token ([CLS]) is added to the beginning of the sentence and is used to calculate an aggregated representation of the statement [11,64,91,84,98]. -Representing requirements by representing its main "semantic frames" which can be retrieved with the help of FrameNet [142].…”

Section: Advanced Embedding Representationsmentioning

confidence: 99%

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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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Section: Requirements Extractionmentioning

confidence: 99%

Section: Advanced Embedding Representationsmentioning

confidence: 99%

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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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“…Huayao Wu et al. [27] applied a textual pattern‐based approach and classifier to extract features from Chinese App descriptions, determined user reviews that are relevant to features, and applied a multi‐linear regression model to identify key features. To find the relationship between App description and App review, Timo et al.…”

Section: Related Workmentioning

confidence: 99%

“…To support App Store mining and analysis, Harman et al [6] identified features in the App descriptions according to some pattern templates, and performed a word frequency and co-location analysis to extract such information, then they further studied the life-cycles of features [25] and predicted App rating using App features [26]. Huayao Wu et al [27] applied a textual pattern-based approach and classifier to extract features from Chinese App descriptions, determined user reviews that are relevant to features, and applied a multi-linear regression model to identify key features. To find the relationship between App description and App review, Timo et al [7] defined 18 part-of-speech patterns and extracted features from App descriptions and reviews according to those patterns.…”

Section: Feature Extractionmentioning

confidence: 99%

Mining detailed information from the description for App functions comparison

et al. 2021

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The rapid development of Apps not only brings huge economic benefit but also causes increasingly fierce competition. In such a situation, developers are required to develop and update innovative functions to attract and retain users. Afterwards, analysing the functions of similar products can help developers formulate a well-designed plan at the beginning of development as well as make updated strategies during the version update process. However, although there have been some methods that can be applied to extract the features from App descriptions to achieve this purpose to some extent, the features they obtained do not cover the details of App functions. Therefore, to conduct an in-depth research on App functions, a novel method is proposed to extract App features with detailed information and an approach to integrate the gained results for further helping developers obtain the valuable knowledge better is provided. Subsequently, a series of experiments is carried out to evaluate our method. The results reveal that the proposed method can mine the features with detailed information from descriptions and integrate them effectively and also can assist developers to compare with other competitors and develop a better competitive analysis scheme.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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ReviewLocator: Enhance User Review-Based Bug Localization with Bug Reports

Xiao,

et al. 2023

Lecture Notes in Computer Science

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Identifying Key Features from App User Reviews

Cited by 26 publications

References 38 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

Mining detailed information from the description for App functions comparison

ReviewLocator: Enhance User Review-Based Bug Localization with Bug Reports

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