Reproducing Context-sensitive Crashes of Mobile Apps using Crowdsourced Monitoring

María Gómez 1 Romain Rouvoy 1 Bram Adams 2 Lionel Seinturier 3, 1
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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.
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https://hal.inria.fr/hal-01276926
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María Gómez, Romain Rouvoy, Bram Adams, Lionel Seinturier. Reproducing Context-sensitive Crashes of Mobile Apps using Crowdsourced Monitoring. IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft'16), May 2016, Austin, Texas, United States. ⟨hal-01276926⟩

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