Dynamic Slicing for Android

Document Type

Conference Proceeding

Publication Date

5-1-2019

Abstract

Dynamic program slicing is useful for a variety of tasks, from testing to debugging to security. Prior slicing approaches have targeted traditional desktop/server platforms, rather than mobile platforms such as Android. Slicing mobile, event-based systems is challenging due to their asynchronous callback construction and the IPC (interprocess communication)- heavy, sensor-driven, timing-sensitive nature of the platform. To address these problems, we introduce AndroidSlicer1, the first slicing approach for Android. AndroidSlicer combines a novel asynchronous slicing approach for modeling data and control dependences in the presence of callbacks with lightweight and precise instrumentation; this allows slicing for apps running on actual phones, and without requiring the app's source code. Our slicer is capable of handling a wide array of inputs that Android supports without adding any noticeable overhead. Experiments on 60 apps from Google Play show that AndroidSlicer is effective (reducing the number of instructions to be examined to 0.3% of executed instructions) and efficient (app instrumentation and post-processing combined takes 31 seconds); all while imposing a runtime overhead of just 4%. We present three applications of AndroidSlicer that are particularly relevant in the mobile domain: (1) finding and tracking input parts responsible for an error/crash, (2) fault localization, i.e., finding the instructions responsible for an error/crash, and (3) reducing the regression test suite. Experiments with these applications on an additional set of 18 popular apps indicate that AndroidSlicer is effective for Android testing and debugging.

Identifier

85072286205 (Scopus)

ISBN

[9781728108698]

Publication Title

Proceedings International Conference on Software Engineering

External Full Text Location

https://doi.org/10.1109/ICSE.2019.00118

ISSN

02705257

First Page

1154

Last Page

1164

Volume

2019-May

Grant

CNS- 1617584

Fund Ref

National Science Foundation

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