PADRAIG: Precise Android Automated Input Generation

2026-07-31

AI Summary

This work addresses the inefficiency and insufficient coverage of input generation in Android automated testing by proposing a model-driven test input generation framework grounded in fine-grained control flow modeling. By constructing precise control flow models and integrating them with an efficient test generation strategy, the approach substantially enhances both the coverage and generation efficiency of test cases. Empirical evaluation on 19 F-Droid applications demonstrates that the proposed framework achieves an average 16% improvement in line coverage compared to existing tools while reducing test generation time by 84%, thereby validating its effectiveness and innovation in simultaneously optimizing coverage and efficiency.

Abstract

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.

Problem

• Android automated testing
• test input generation
• code coverage
• generation runtime
• model-based testing

Innovation

• model-based testing
• Android automated testing
• control flow model
• test input generation
• code coverage