Prioritizing user concerns in app reviews – A study of requests for new features, enhancements and bug fixes

Malgaonkar, Saurabh; Licorish, Sherlock A.; Savarimuthu, Bastin Tony Roy

doi:10.1016/j.infsof.2021.106798

Cited by 18 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies on user gender ( Noei & Lyons, 2022 ) and findings of children abused or in unsafe environments ( Dalvi, Siddavatam, Thakkar, Vedpathak, & Patel, 2022 ) can be mined using app reviews. Survey on app reviews study also found on literature, such as analysis on COVID-19 contract-tracing apps ( Garousi, Cutting, & Felderer, 2022 ), and features enhancement, bug fixes, performance analysis using app reviews analysis ( Gao, Guo et al, 2022 , Kim and Kim, 2022 , Malgaonkar et al, 2022 ). Also, analysis of sentiment of user reviews of apps is necessarily used in mining information for different problem areas such as Bonny et al, 2022 , Hossain et al, 2022 , Hossen et al, 2022 , Mahmud et al, 2022 , Ula and Utami, 2022 and Yang, Gao, Zang, Lo, and Lyu (2021) .…”

Section: Related Workmentioning

confidence: 93%

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

Islam

Datta

Iqbal

2023

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 93%

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

Islam

Datta

Iqbal

2023

Expert Systems with Applications

View full text Add to dashboard Cite

“…User feedback plays an essential role in serving as a major channel between developers and users, reflecting new feature requirements, enhancements in the user interface, and reporting serious app bugs [48]. For many years, researchers from academia and industry have explored mining app reviews for assisting different stages of app development and maintenance, such as prioritizing app reviews [1]- [3], [21], [49], predicting app feature liked/disliked by users [7], [8], classifying app reviews [4]- [6], and identifying emerging app issues [9], [10].…”

Section: B App Review Miningmentioning

confidence: 99%

“…Then they designed a framework to track reviews over the release versions of the app and recommend phrase-level issues of an app to its developers. Malgaonkar et al [49] studied recent works on app review prioritization and developed a multi-criteria heuristic model for identifying and prioritizing informative reviews. For predicting app features liked/disliked by users, Gu et al [7] and Guzman et al [8] proposed to classify reviews into predefined categories and extracts aspects in sentences that include evaluation of aspect using natural language processing techniques.…”

Section: B App Review Miningmentioning

confidence: 99%

Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

Gao¹,

Li²,

Qi³

et al. 2022

Preprint

View full text Add to dashboard Cite

App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning, however, most of the studies strongly rely on pre-defined classes or manually-annotated reviews. Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously.In the paper, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. The framework mainly contains three modules: The review helpfulness prediction module, topic-sentiment modeling module, and multi-factor ranking module. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multi-factor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning.

show abstract

“…Opinions extracted from informative end-user reviews provide a wide range of user feedback to support software engineering activities, such as bug report classification, new feature requests, usage experience, or enhancements (i.e., suggestions for improvements) (Martin et al, 2016;AlSubaihin et al, 2019;Dabrowski et al, 2020;Araujo and Marcacini, 2021;Malgaonkar et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, developers can investigate issues to comprehend the user's concerns about a faulty feature or compromised user experience and potentially fix or improve it more quickly, i.e., before impacting many users and negatively affecting the app's ratings (Lima et al, 2022). In view of this dynamic environment and a large amount of data, the problem of detecting and prioritizing issues from reviews is crucial and remains for both practitioners and researchers (Licorish et al, 2017;Groen et al, 2015;Malgaonkar et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Issue detection and prioritization based on app reviews

Lima

Barbosa

Marcacini

2023

Preprint

View full text Add to dashboard Cite

Opinion mining for app reviews aims to analyze people's comments on app stores to support software engineering activities, particularly software maintenance and evolution. One of the main challenges for software quality maintenance is promptly identifying emerging issues, e.g., bugs. However, due to a large amount of textual data, manually analyzing these comments is challenging, and machine learning-based methods have been used to automate opinion mining. Our objective is to minimize the time between the occurrence of the issue and its correction, providing means for the quick identification of the issue through prioritization levels. This paper introduces the automatic generation of a risk matrix from user reviews to answer the following research question: how do we prioritize and treat reviews in time so that the app is competitive and guarantees the timely maintenance and evolution of the software? We present the MApp-IDEA (Monitoring App for Issue Detection and Prioritization) method to detect issues and classify the reviews in a risk matrix with prioritization levels. We present an approach that (i) automatically collects reviews, (ii) detects issues, (iii) classifies reviews in a risk matrix, and then (iv) models the temporal dynamics of issues and risks through time series to trigger alerts. The results showed that our unsupervised issue detection approach is competitive compared to state-of-the-art supervised methods. We performed an empirical evaluation with 50 mobile applications (apps) and processed approximately 5 million reviews, where we detected 230,000 issues and classified them into priority levels using a risk matrix. We have shown that opinions extracted from user comments provide important information about the app's issues and risks. In evaluation, we found that issues detected early with our approach are associated with later fix releases by developers.

show abstract

Prioritizing user concerns in app reviews – A study of requests for new features, enhancements and bug fixes

Cited by 18 publications

References 40 publications

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

Issue detection and prioritization based on app reviews

Contact Info

Product

Resources

About