arXiv subjects

Jordan Doyle

Publications and source records attributed to Jordan Doyle.

3 recordsLinked to original sources

arXiv2026

Modelling Android applications through static analysis and systematic exploratory testing

Mobile application development is a fast paced industry with frequent releases. While the development pace increases, so too does the need for automated test generation. Model-based test generation is one of the most common and successful approaches to support this need. Understanding and modelling the application under test is integral to producing comprehensive, dependable and effective tests. Unfortunately, mobile platforms such as Android, introduce a host of difficulties. Static analysis struggles with Android's event-based nature and the growing variety of mechanisms available for developers to implement different features. Additionally, dynamic analysis, implemented by popular random test generators, is slow, inefficient, and limited by a lack of application knowledge. This paper introduces DroidGraph, a framework to generate a comprehensive control flow model of Android applications using traditional static analysis and efficient systematic exploratory tests. DroidGraph provides a detailed model of an Android application, from low level method statements to high level user interface structures. This model can be used to support automated test generation. We apply DroidGraph to 19 diverse apps and show that our efficient exploratory tests, on average, interact with 18% more of the app than commonly used random exploration in 345 less interactions. Integrating the dynamic analysis results provided by these tests complements our static analysis, and uncovers on average 51 more components and 49% more interface callback links in the application code.

cs.SE↗

arXiv2026

Improving Mobile User Interface Testing with Model Driven Monkey Search

Testing mobile applications often relies on tools, such as Exerciser Monkey for Android systems, that simulate user input. Exerciser Monkey, for example, generates random events (e.g., touches, gestures, navigational keys) that give developers a sense of what their application will do when deployed on real mobile phones with real users interacting with it. These tools, however, have no knowledge of the underlying applications' structures and only interact with them randomly or in a predefined manner (e.g., if developers designed scenarios, a labour-intensive task) -- making them slow and poor at finding bugs. In this paper, we propose a novel control flow structure able to represent the code of Android applications, including all the interactive elements. We show that our structure can increase the effectiveness (higher coverage) and efficiency (removing duplicate/redundant tests) of the Exerciser Monkey by giving it knowledge of the test environment. We compare the interface coverage achieved by the Exerciser Monkey with our new Monkey++ using a depth first search of our control flow structure and show that while the random nature of Exerciser Monkey creates slow test suites of poor coverage, the test suite created by a depth first search is one order of magnitude faster and achieves full coverage of the user interaction elements. We believe this research will lead to a more effective and efficient Exerciser Monkey, as well as better targeted search based techniques for automated Android testing.

cs.SE↗

arXiv2026

PADRAIG: Precise Android Automated Input Generation

Android automated test input generation has been a highly researched topic for over a decade and has shown promising results with a variety of approaches. Random input generation is commonly used and the easiest to maintain, but ultimately inefficient. Systematic and search-based approaches produce effective tests but require a disproportionally large generation runtime. Model-based approaches have the additional overhead of modelling the application under test (AUT) but they result in a faster test generation. In this paper we present Precise AnDRoid Automated Input Generation (PADRAIG), a model-based test input generation framework that uses a detailed control flow model of the AUT to generate tests that can achieve higher line coverage, with a lower test generation runtime than the state of the art. We compare the line coverage achieved, and the generation runtime of PADRAIG against 3 state of the art tools, each of which uses a different test input generation technique. Our results, using 19 randomly selected Android apps from the F-Droid application store, show that PADRAIG achieves, on average, 16% more coverage of the AUT than the state of the art and it can generate tests with, on average, 84% less runtime.

cs.SE↗