Release planning of mobile apps based on user reviews

Villarroel, Lorenzo; Bavota, Gabriele; Russo, Barbara; Oliveto, Rocco; Penta, Massimiliano Di

doi:10.1145/2884781.2884818

Cited by 221 publications

(196 citation statements)

References 19 publications

Supporting

Mentioning

190

Contrasting

Unclassified

Order By: Relevance

“…For grouping similar user feedback, LDA is one of the most used algorithms [4], [15]. The work most related to ours is that by Chen et al [2], Villarroel et al [16] and Di Sorbo et al [17]. All of them present approaches to classify, group and rank app store user reviews automatically and use similar techniques to the ones presented in this work: supervised machine learning for classifying, topic modeling or clustering for grouping, and a scoring function or machine learning for ranking.…”

Section: Related Workmentioning

confidence: 89%

“…b) Training and Comparisons: We trained and tested the classifiers with a 10-fold cross validation on the previously described truthset. Furthermore, we compared the results of the Multinomial Naive Bayes classifier against the results of a Random Forest classifier, which had good results when classifying user feedback from app reviews [16]. The training and evaluation of the classifiers was performed using Weka 5 .…”

Section: B Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Guzmán

Ibrahim

Glinz

2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

101

View full text Add to dashboard Cite

Abstract-Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number-in the range of thousands per day for popular applications-a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.

show abstract

Section: Related Workmentioning

confidence: 89%

Section: B Classificationmentioning

confidence: 99%

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Guzmán

Ibrahim

Glinz

2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

101

View full text Add to dashboard Cite

show abstract

“…In the context of mobile development, recent work proved the usefulness of user review information for the planning of maintenance tasks, providing tools for achieving this goal [4], [15], [17], [19], [22]. Specifically, they show that feedback posted on mobile app stores represents an unmatched source for developers seeking for defects in their applications [6], [17], [19].…”

Section: Introductionmentioning

confidence: 99%

BECLoMA: Augmenting stack traces with user review information

Pelloni

Grano

Ciurumelea

et al. 2018

2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

Mobile devices such as smartphones, tablets and wearables are changing the way we do things, radically modifying our approach to technology. To sustain the brutal competition in the mobile market, developers need to deliver high quality applications in a short release cycle. Therefore, to maximize their market success, they aim to reveal and fix bugs as soon as possible. For this reason, researchers and practitioners proposed testing tools to automate the process of bug discovery and fixing. In the mobile development context, the content of user reviews represents an unmatched source for developers seeking for defects in their applications. However, no prior work explored the adoption of information available in user reviews for testing purposes. In this demo we present BECLoMA, a tool to enable the integration of user feedback in the testing process of mobile apps. BECLoMA links information from testing tools and user reviews, presenting developers an augmented testing report combining stack traces with user reviews information referring to the same crash. We show that BECLoMA facilitates not only the diagnosis and fix of app bugs, but also presents additional benefits: it eases the usage of testing tools and automates the analysis of user reviews from the Google Play Store. Abstract-Mobile devices such as smartphones, tablets and wearables are changing the way we do things, radically modifying our approach to technology. To sustain the high competition characterizing the mobile market, developers need to deliver high quality applications in a short release cycle. To reveal and fix bugs as soon as possible, researchers and practitioners proposed tools to automate the testing process. However, such tools generate a high number of redundant inputs, lacking of contextual information and generating reports difficult to analyze. In this context, the content of user reviews represents an unmatched source for developers seeking for defects in their applications. However, no prior work explored the adoption of information available in user reviews for testing purposes. In this demo we present BECLOMA, a tool to enable the integration of user feedback in the testing process of mobile apps. BECLOMA links information from testing tools and user reviews, presenting to developers an augmented testing report combining stack traces with user reviews information referring to the same crash. We show that BECLOMA facilitates not only the diagnosis and fix of app bugs, but also presents additional benefits: it eases the usage of testing tools and automates the analysis of user reviews from the Google Play Store.

show abstract

“…There are high competition among developer that build similar apps. Disapointing and ignoring user will cause the developer a loose in the market share [6]. They do not need to conduct some survey that usually very budget and time consuming.…”

Section: Introductionmentioning

confidence: 99%

Aspect Extraction using Informative Data from Mobile App Data Review

Prasetyo¹,

Putri²,

Pamungkas³

2017

IJCA

View full text Add to dashboard Cite

User review of mobile application is an valuable data that can be used by developer to improve their application or to build similar application. User can give feedback such as reporting errors, asking for new or improved feature, explain their experience of using certain feature and also praise or dispraise. User review or opinion data is very large in amount and difficult to analyze. It is time consuming and labour expensive to do it manually. Recent study has tried to extract product feature using word collocation. In this work, we try to improve the aspect extraction process by using only informative data. We took user opinion of 3 mobile application from application distribution platform. The experiment result indicate that our approach is able to improve the performance of collocation finding method.. General TermsInformation retrieval

show abstract

Release planning of mobile apps based on user reviews

Cited by 221 publications

References 19 publications

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

BECLoMA: Augmenting stack traces with user review information

Aspect Extraction using Informative Data from Mobile App Data Review

Contact Info

Product

Resources

About