@inproceedings{Lu2017344,
	series = {{ACM} {International} {Conference} {Proceeding} {Series}},
	title = {Automatic classification of non-functional requirements from augmented app user reviews},
	volume = {Part F128635},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446065&doi=10.1145%2f3084226.3084241&partnerID=40&md5=b913c3e9c9078f65d2095c4ad24d4adc},
	doi = {10.1145/3084226.3084241},
	abstract = {Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. Research shows that user reviews contain abundant useful information which may help developers to improve their Apps. Extracting and considering Non-Functional Requirements (NFRs), which describe a set of quality attributes wanted for an App and are hidden in user reviews, can help developers to deliver a product which meets users' expectations. Objective: Developers need to be aware of the NFRs from massive user reviews during software maintenance and evolution. Automatic user reviews classification based on an NFR standard provides a feasible way to achieve this goal. Method: In this paper, user reviews were automatically classified into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others. We combined four classification techniques BoW, TF-IDF, CHI2, and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging to classify user reviews. We conducted experiments to compare the F-measures of the classification results through all the combinations of the techniques and algorithms. Results: We found that the combination of AUR-BoW with Bagging achieves the best result (a precision of 71.4},
	author = {Lu, M. and Liang, P.},
	year = {2017},
	note = {tex.author\_keywords: Automatic classification; Non-functional requirements; Textual semantics; User reviews
tex.document\_type: Conference Paper
tex.source: Scopus},
	keywords = {\#nosource},
	pages = {344--353},
}

{"_id":"BHSwTEHuxrBTY4vMi","bibbaseid":"lu-liang-automaticclassificationofnonfunctionalrequirementsfromaugmentedappuserreviews-2017","author_short":["Lu, M.","Liang, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","series":"ACM International Conference Proceeding Series","title":"Automatic classification of non-functional requirements from augmented app user reviews","volume":"Part F128635","url":"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446065&doi=10.1145%2f3084226.3084241&partnerID=40&md5=b913c3e9c9078f65d2095c4ad24d4adc","doi":"10.1145/3084226.3084241","abstract":"Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. Research shows that user reviews contain abundant useful information which may help developers to improve their Apps. Extracting and considering Non-Functional Requirements (NFRs), which describe a set of quality attributes wanted for an App and are hidden in user reviews, can help developers to deliver a product which meets users' expectations. Objective: Developers need to be aware of the NFRs from massive user reviews during software maintenance and evolution. Automatic user reviews classification based on an NFR standard provides a feasible way to achieve this goal. Method: In this paper, user reviews were automatically classified into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others. We combined four classification techniques BoW, TF-IDF, CHI2, and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging to classify user reviews. We conducted experiments to compare the F-measures of the classification results through all the combinations of the techniques and algorithms. Results: We found that the combination of AUR-BoW with Bagging achieves the best result (a precision of 71.4","author":[{"propositions":[],"lastnames":["Lu"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Liang"],"firstnames":["P."],"suffixes":[]}],"year":"2017","note":"tex.author_keywords: Automatic classification; Non-functional requirements; Textual semantics; User reviews tex.document_type: Conference Paper tex.source: Scopus","keywords":"#nosource","pages":"344–353","bibtex":"@inproceedings{Lu2017344,\n\tseries = {{ACM} {International} {Conference} {Proceeding} {Series}},\n\ttitle = {Automatic classification of non-functional requirements from augmented app user reviews},\n\tvolume = {Part F128635},\n\turl = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446065&doi=10.1145%2f3084226.3084241&partnerID=40&md5=b913c3e9c9078f65d2095c4ad24d4adc},\n\tdoi = {10.1145/3084226.3084241},\n\tabstract = {Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. 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