Paper Info
Title
Predicting Crashing Releases of Mobile Applications.
Abstract
Context: The quality of mobile applications has a vital impact on their user's experience, ratings and ultimately overall success. Given the high competition in the mobile application market, i.e., many mobile applications perform the same or similar functionality, users of mobile apps tend to be less tolerant to quality issues. Goal: Therefore, identifying these crashing releases early on so that they can be avoided will help mobile app developers keep their user base and ensure the overall success of their apps. Method: To help mobile developers, we use machine learning techniques to effectively predict mobile app releases that are more likely to cause crashes, i.e., crashing releases. To perform our prediction, we mine and use a number of factors about the mobile releases, that are grouped into six unique dimensions: complexity, time, code, diffusion, commit, and text, and use a Naive Bayes classified to perform our prediction. Results: We perform an empirical study on 10 open source mobile applications containing a total of 2,638 releases from the F-Droid repository. On average, our approach can achieve F1 and AUC scores that improve over a baseline (random) predictor by 50% and 28%, respectively. We also find that factors related to text extracted from the commit logs prior to a release are the best predictors of crashing releases and have the largest effect. Conclusions: Our proposed approach could help to identify crash releases for mobile apps.
Year
DOI
Venue
2016
10.1145/2961111.2962606
ESEM
Keywords
Field
DocType
Mobile Applications, Crash Release, Prediction Model
Crash,Mobile app,World Wide Web,Naive Bayes classifier,Computer science,Commit,Mobile apps,Empirical research
Conference
Citations 
PageRank 
References 
16
0.54
44
Authors
5
Name
Order
Citations
PageRank
Xin Xia197265.97
Emad Shihab2122954.74
Yasutaka Kamei391050.52
David Lo45346259.67
xinyu559030.19