Mining Android App Usages for Generating Actionable GUI-based Execution Scenarios
Mario Linares-Vasquez, Martin White, Carlos Bernal-Cardenas, Kevin, Moran, and Denys Poshyvanyk

TL;DR
MonkeyLab is a hybrid framework that mines GUI-based models from Android app traces to generate realistic and corner-case execution scenarios, improving testing coverage and automation.
Contribution
It introduces a novel hybrid approach combining static/dynamic analysis and language modeling to generate feasible, replayable scenarios including unseen events.
Findings
Successfully generated scenarios for multiple Android apps
Able to produce both natural and corner-case execution sequences
Validated feasibility and replayability on real devices
Abstract
GUI-based models extracted from Android app execution traces, events, or source code can be extremely useful for challenging tasks such as the generation of scenarios or test cases. However, extracting effective models can be an expensive process. Moreover, existing approaches for automatically deriving GUI-based models are not able to generate scenarios that include events which were not observed in execution (nor event) traces. In this paper, we address these and other major challenges in our novel hybrid approach, coined as MonkeyLab. Our approach is based on the Record-Mine-Generate-Validate framework, which relies on recording app usages that yield execution (event) traces, mining those event traces and generating execution scenarios using statistical language modeling, static and dynamic analyses, and validating the resulting scenarios using an interactive execution of the app on…
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Taxonomy
TopicsMobile and Web Applications · Software System Performance and Reliability · Advanced Malware Detection Techniques
