Paper Info
Title
Can app changelogs improve requirements classification from app reviews?: an exploratory study
Abstract
[Background] Recent research on mining app reviews for software evolution indicated that the elicitation and analysis of user requirements can benefit from supplementing user reviews by data from other sources. However, only a few studies reported results of leveraging app changelogs together with app reviews. [Aims] Motivated by those findings, this exploratory experimental study looks into the role of app changelogs in the classification of requirements derived from app reviews. We aim at understanding if the use of app changelogs can lead to more accurate identification and classification of functional and non-functional requirements from app reviews. We also want to know which classification technique works better in this context. [Method] We did a case study on the effect of app changelogs on automatic classification of app reviews. Specifically, manual labeling, text preprocessing, and four supervised machine learning algorithms were applied to a series of experiments, varying in the number of app changelogs in the experimental data. [Results] We compared the accuracy of requirements classification from app reviews, by training the four classifiers with varying combinations of app reviews and changelogs. Among the four algorithms, Naïve Bayes was found to be more accurate for categorizing app reviews. [Conclusions] The results show that official app changelogs did not contribute to more accurate identification and classification of requirements from app reviews. In addition, Naïve Bayes seems to be more suitable for our further research on this topic.
Year
DOI
Venue
2018
10.1145/3239235.3267428
PROCEEDINGS OF THE 12TH ACM/IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM 2018)
Keywords
Field
DocType
App reviews, app changelogs, requirements analysis, machine learning, data-driven requirements engineering
Data mining,Naive Bayes classifier,Experimental data,Computer science,Requirements analysis,Preprocessor,Artificial intelligence,Software evolution,User requirements document,Exploratory research,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-5823-1
0
0.34
References 
Authors
8
5
Name
Order
Citations
PageRank
Chong Wang1199.62
Fan Zhang222969.82
Peng Liang357049.57
Maya Daneva465865.33
Marten J. van Sinderen516514.72