PlayCoder raises the rate of LLM-generated GUI apps that can be played end-to-end without logic errors from near zero to 20.3% Play@3 by adding repository-aware generation, agent-driven testing, and iterative repair.
Chatting with GPT-3 for zero-shot human-like mobile automated GUI testing
4 Pith papers cite this work. Polarity classification is still indexing.
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WebTestPilot symbolizes GUI elements to infer contextual oracles for end-to-end web testing from natural language specs, reporting 99% task completion and 96% precision/recall on a new bug-injected benchmark.
Proactive multi-window state triggering plus Set-of-Mark alignment and multimodal LLM reasoning detects GUI defects in Android apps, reporting 184% more text truncation, 87.2% F1 on occlusion, and 40 defect-prone apps at 10% FPR.
DynamicsLLM uses LLMs to generate execution traces that cover three times more code smell-related events than the prior Dynamics tool on 333 F-Droid Android apps, with a hybrid method adding 25.9% coverage for low-activity apps.
citing papers explorer
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PlayCoder: Making LLM-Generated GUI Code Playable
PlayCoder raises the rate of LLM-generated GUI apps that can be played end-to-end without logic errors from near zero to 20.3% Play@3 by adding repository-aware generation, agent-driven testing, and iterative repair.
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WebTestPilot: Agentic End-to-End Web Testing against Natural Language Specification by Inferring Oracles with Symbolized GUI Elements
WebTestPilot symbolizes GUI elements to infer contextual oracles for end-to-end web testing from natural language specs, reporting 99% task completion and 96% precision/recall on a new bug-injected benchmark.
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Proactive Detection of GUI Defects in Multi-Window Scenarios via Multimodal Reasoning
Proactive multi-window state triggering plus Set-of-Mark alignment and multimodal LLM reasoning detects GUI defects in Android apps, reporting 184% more text truncation, 87.2% F1 on occlusion, and 40 defect-prone apps at 10% FPR.
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DynamicsLLM: a Dynamic Analysis-based Tool for Generating Intelligent Execution Traces Using LLMs to Detect Android Behavioural Code Smells
DynamicsLLM uses LLMs to generate execution traces that cover three times more code smell-related events than the prior Dynamics tool on 333 F-Droid Android apps, with a hybrid method adding 25.9% coverage for low-activity apps.