The development of Large Language Models (LLMs) enables LLM-based GUI testing to interact with graphical user interfaces by understanding GUI screenshots and generating actions, which are widely applied in industry and academia. However, current approaches test each app in isolation, lacking mechanisms for experience accumulation and reuse. This limitation often causes GUI testing approaches to miss deeper exploration and fail to trigger bug-prone functionalities. To address this, we propose MemoDroid, a three-layer memory mechanism that augments LLM-based GUI testing with the ability to evolve through repeated interaction. MemoDroid designs episodic memory to capture functional-level testing traces, reflective memory to summarize issue patterns and redundant behaviors, and strategic memory to synthesize cross-app exploration strategies. These memory layers are dynamically retrieved and injected into LLM prompts at runtime, enabling the agent to reuse successful behaviors, avoid ineffective actions, and prioritize bug-prone paths. We implement MemoDroid as a lightweight plugin, which can be integrated into existing LLM-based GUI testing approaches. We evaluate MemoDroid on real-world apps from 15 diverse app categories. Results show that MemoDroid enhances GUI testing performance across five baselines, with activity and code coverage increasing by 79% - 96% and 81% - 97%, and bug detection improving by 57% - 198%. Ablation studies confirm the contributions of each memory layer. Furthermore, MemoDroid detects 49 new bugs in 200 popular apps, with 35 confirmed fixes and 14 acknowledged by developers, showing its practical value in memory-driven GUI testing.

Mobile app GUI (Graphical User Interface) pages now contain rich visual information, with the visual semantics of each page helping users understand the application logic. However, these complex visual and functional logics present new challenges to software testing. Existing automated GUI testing methods, constrained by the lack of reliable testing oracles, are limited to detecting crash bugs with obvious abnormal signals. Consequently, many non-crash functional bugs, ranging from unexpected behaviors to logical errors, often evade detection by current techniques. While these non-crash functional bugs can exhibit visual cues that serve as potential testing oracles, they often entail a sequence of screenshots, and detecting them necessitates an understanding of the operational logic among GUI page transitions, which is challenging traditional techniques. Considering the remarkable performance of Multimodal Large Language Models (MLLM) in visual and language understanding, this paper proposesVisionDroid, a novel vision-driven, multi-agent collaborative automated GUI testing approach for detecting non-crash functional bugs. It comprises three agents: Explorer, Monitor, and Detector, to guide the exploration, oversee the testing progress, and spot issues.We also address several challenges,i.e., aligning visual and textual information for MLLM input, achieving functionality-oriented exploration, and inferring test oracles for non-crash bugs, to enhance the performance of functionality bug detection. We evaluateVisionDroidon 590 non-crash bugs and compare it with 12 baselines, it can achieve more than 14%-112% and 108%-147% boost in average recall and precision compared with the best baseline. The ablation study further proves the contribution of each module. Moreover,VisionDroididentifies 43 unknown bugs on Google Play, of which 31 have been fixed.

Deep reinforcement learning has achieved remarkable success in competitive games, surpassing human performance in applications ranging from business competitions to video games. In competitive environments, agents face the challenge of adapting to continuously shifting adversary strategies, necessitating the ability to handle diverse scenarios. Existing studies primarily focus on evaluating agent robustness either through perturbing observations, which has practical limitations, or through training adversarial agents to expose weaknesses, which lacks strategy diversity exploration. There are also studies which rely on curiosity-based mechanism to explore the diversity, yet they may lack direct guidance to enhance identified decision-making flaws. In this paper, we propose a novel diversity-oriented testing framework (called AdvTest) to test the competitive game agent via constraint-guided adversarial agent training. Specifically, AdvTest adds constraints as the explicit guidance during adversarial agent training to make it capable of defeating the target agent using diverse strategies. To realize the method, three challenges need to be addressed, i.e., what are the suitable constraints, when to introduce constraints, and which constraint should be added. We experimentally evaluate AdvTest on the commonly-used competitive game environment, StarCraft II. The results on four maps show that AdvTest exposes more diverse failure scenarios compared with the commonly-used and state-of-the-art baselines.

Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind visually impaired users what to fill in. Unfortunately, based on our analysis of 4,501 Android apps with text inputs, over 76% of them are missing hint-text. These issues are mostly caused by developers’ lack of awareness when considering visually impaired individuals. To overcome these challenges, we developed an LLM-based hint-text generation model called HintDroid, which analyzes the GUI information of input components and uses in-context learning to generate the hint-text. To ensure the quality of hint-text generation, we further designed a feedback-based inspection mechanism to further adjust hint-text. The automated experiments demonstrate the high BLEU and a user study further confirms its usefulness. HintDroid can not only help visually impaired individuals, but also help ordinary people understand the requirements of input components. HintDroid demo video: https://youtu.be/FWgfcctRbfI.

Automated Graphical User Interface (GUI) testing plays a crucial role in ensuring app quality, especially as mobile applications have become an integral part of our daily lives. Despite the growing popularity of learning-based techniques in automated GUI testing due to their ability to generate human-like interactions, they still suffer from several limitations, such as low testing coverage, inadequate generalization capabilities, and heavy reliance on training data. Inspired by the success of Large Language Models (LLMs) like ChatGPT in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within this framework, we have also introduced a functionality-aware memory prompting mechanism that equips the LLM with the ability to retain testing knowledge of the whole process and conduct long-term, functionality-based reasoning to guide exploration. We evaluate it on 93 apps from Google Play and demonstrate that it outperforms the best baseline by 32% in activity coverage, and detects 31% more bugs at a faster rate. Moreover, GPTDroid identifies 53 new bugs on Google Play, of which 35 have been confirmed and fixed.

Mobile applications have become a ubiquitous part of our daily life, providing users with access to various services and utilities. Text input, as an important interaction channel between users and applications, plays an important role in core functionality such as search queries, authentication, messaging, etc. However, certain special text (e.g., -18 for Font Size) can cause the app to crash, and generating diversified unusual inputs for fully testing the app is highly demanded. Nevertheless, this is also challenging due to the combination of explosion dilemma, high context sensitivity, and complex constraint relations. This paper proposes InputBlaster which leverages the LLM to automatically generate unusual text inputs for mobile app crash detection. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. In detail, InputBlaster leverages LLM to produce the test generators together with the mutation rules serving as the reasoning chain, and utilizes the in-context learning schema to demonstrate the LLM with examples for boosting the performance. InputBlaster is evaluated on 36 text input widgets with cash bugs involving 31 popular Android apps, and results show that it achieves 78% bug detection rate, with 136% higher than the best baseline. Besides, we integrate it with the automated GUI testing tool and detect 37 unseen crashes in real-world apps.

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this paper, to highlight a typical balance between exploration (diversifying behaviors) and exploitation (detecting failures), we propose an advanced testing framework for MAS called with diversity-guided exploration and adaptive critical state exploitation. It incorporates both individual diversity and team diversity, and designs an adaptive perturbation mechanism to perturb the action at the critical states, so as to trigger more and more diverse failure scenarios of the system. We evaluate MASTest on two popular MAS simulation environments: Coop Navi and StarCraft II. Results show that the average distance of the resulting failure scenarios is increased by 29.55%-103.57% and 74.07%-370.00% on two environments compared to the baselines. Also, the failure patterns found by MASTest are improved by 71.44%-300.00% and 50%-500.00% on two experimental environments compared to the baselines.