Predicting crashing releases of mobile applications

Xin Xia, Emad Shihab, Yasutaka Kamei, David Lo, Xinyu Wang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

43 Citations (Scopus)


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.

Original languageEnglish
Title of host publication10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2016
Subtitle of host publication8th and 9th September 2016 Ciudad Real, SPAIN
EditorsAndreas Jedlitschka , Magne Jørgensen
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450344272
Publication statusPublished - 2016
Externally publishedYes
EventInternational Symposium on Empirical Software Engineering and Measurement 2016 - Ciudad Real, Spain
Duration: 8 Sept 20169 Sept 2016
Conference number: 10th


ConferenceInternational Symposium on Empirical Software Engineering and Measurement 2016
Abbreviated titleESEM 2016
CityCiudad Real
Internet address


  • Crash Release
  • Mobile Applications
  • Prediction Model

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