Reproducing Context-sensitive Crashes of ...
Document type :
Communication dans un congrès avec actes
Title :
Reproducing Context-sensitive Crashes of Mobile Apps using Crowdsourced Monitoring
Author(s) :
Gómez, María [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Adams, Bram [Auteur]
École Polytechnique de Montréal [EPM]
Seinturier, Lionel [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Institut universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]

Self-adaptation for distributed services and large software systems [SPIRALS]
Adams, Bram [Auteur]
École Polytechnique de Montréal [EPM]
Seinturier, Lionel [Auteur]

Self-adaptation for distributed services and large software systems [SPIRALS]
Institut universitaire de France [IUF]
Scientific editor(s) :
Lori Flynn
Paola Inverardi
Paola Inverardi
Conference title :
IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft'16)
City :
Austin, Texas
Country :
Etats-Unis d'Amérique
Start date of the conference :
2016-05-16
Journal title :
Proceedings of the 3rd IEEE/ACM International Conference on Mobile Software Engineering and Systems
Publisher :
IEEE
Publication date :
2016-05-16
English keyword(s) :
Mobile app crash reproduction
Context-sensitive crashes
Crowdsourcing
Android apps
Context-sensitive crashes
Crowdsourcing
Android apps
HAL domain(s) :
Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]/Informatique ubiquitaire
Informatique [cs]/Informatique mobile
Informatique [cs]/Informatique ubiquitaire
Informatique [cs]/Informatique mobile
English abstract : [en]
While the number of mobile apps published by app stores keeps on increasing, the quality of these apps varies widely. Unfortunately, for many apps, end-users continue experiencing bugs and crashes once installed on their ...
Show more >While the number of mobile apps published by app stores keeps on increasing, the quality of these apps varies widely. Unfortunately, for many apps, end-users continue experiencing bugs and crashes once installed on their mobile device. While this is annoying for the end users, it definitely is for the developers of an app, as they need to determine as fast as possible how to reproduce reported crashes before finding the root cause of the crashes. Given the heterogeneity in hardware, mobile platform releases, and types of users, the reproduction step currently is one of the major challenges of app developers. This paper therefore introduces MoTiF, a crowdsourced approach to support developers in automatically reproducing context-sensitive crashes faced by end-users in the wild. In particular, by analyzing recurrent patterns in crash data, the shortest sequence of events reproducing a crash is derived, and turned into a test suite. We evaluate MoTiF on concrete crashes that were crowdsourced or randomly generated on 5 Android apps, showing that MoTiF can reproduce existing crashes effectively.Show less >
Show more >While the number of mobile apps published by app stores keeps on increasing, the quality of these apps varies widely. Unfortunately, for many apps, end-users continue experiencing bugs and crashes once installed on their mobile device. While this is annoying for the end users, it definitely is for the developers of an app, as they need to determine as fast as possible how to reproduce reported crashes before finding the root cause of the crashes. Given the heterogeneity in hardware, mobile platform releases, and types of users, the reproduction step currently is one of the major challenges of app developers. This paper therefore introduces MoTiF, a crowdsourced approach to support developers in automatically reproducing context-sensitive crashes faced by end-users in the wild. In particular, by analyzing recurrent patterns in crash data, the shortest sequence of events reproducing a crash is derived, and turned into a test suite. We evaluate MoTiF on concrete crashes that were crowdsourced or randomly generated on 5 Android apps, showing that MoTiF can reproduce existing crashes effectively.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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