Automated GUI testing is crucial for ensuring the quality and reliability of Android apps. However, the efficacy of existing UI testing techniques is often limited, especially in terms of coverage. Recent studies, including the state-of-the-art, struggle to achieve more than 30% activity coverage in real-world apps. This limited coverage can be attributed to a combination of factors such as failing to generate complex user inputs, unsatisfied activation conditions regarding device configurations and external resources, and hard-to-reach code paths that are not easily accessible through the GUI. To overcome these limitations, we propose CovAgent, a novel agentic AI-powered approach to enhance Android app UI testing. Our fuzzer-agnostic framework comprises an AI agent that inspects the app's decompiled Smali code and component transition graph, and reasons about unsatisfied activation conditions within the app code logic that prevent access to the activities that are unreachable by standard and widely adopted GUI fuzzers. Then, another agent generates dynamic instrumentation scripts that satisfy activation conditions required for successful transitions to those activities. We found that augmenting existing fuzzing approaches with our framework achieves a significant improvement in test coverage over the state-of-the-art, LLMDroid, and other baselines such as Fastbot and APE (e.g., 101.1%, 116.3% and 179.7% higher activity coverage, respectively). CovAgent also outperforms all the baselines in other metrics such as class, method, and line coverage. We also conduct investigations into components within CovAgent to reveal further insights regarding the efficacy of Agentic AI in the field of automated app testing such as the agentic activation condition inference accuracy, and agentic activity-launching success rate.

翻译：自动化图形用户界面测试对于确保Android应用的质量与可靠性至关重要。然而，现有UI测试技术的有效性往往受限，尤其是在覆盖率方面。包括最先进技术在内的近期研究，在真实世界应用中难以实现超过30%的活动覆盖率。这种有限的覆盖率可归因于多种因素的综合影响，例如：未能生成复杂的用户输入、涉及设备配置和外部资源的激活条件未满足，以及难以通过GUI访问的代码路径。为克服这些限制，我们提出了CovAgent，一种新颖的基于智能体人工智能的方法，用于增强Android应用UI测试。我们与模糊测试器无关的框架包含一个AI智能体，该智能体检查应用反编译的Smali代码和组件转换图，并推理应用代码逻辑中未满足的激活条件，这些条件阻碍了访问标准且广泛采用的GUI模糊测试器无法到达的活动。随后，另一个智能体生成动态插桩脚本，以满足成功转换至这些活动所需的激活条件。我们发现，将现有模糊测试方法与我们的框架结合，在测试覆盖率上相比最先进技术LLMDroid及其他基线方法（如Fastbot和APE）实现了显著提升（例如，活动覆盖率分别提高了101.1%、116.3%和179.7%）。CovAgent在类覆盖率、方法覆盖率和行覆盖率等其他指标上也优于所有基线方法。我们还对CovAgent内部的组件进行了研究，以进一步揭示智能体人工智能在自动化应用测试领域有效性的深入见解，例如智能体激活条件推断的准确性和智能体活动启动成功率。