PropGen automates property generation for Android app testing via LLM synthesis from guided exploration and feedback refinement, yielding 912 valid properties and 25 previously unknown bugs across 12 apps.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.SE 4years
2026 4representative citing papers
Traditional ML models on bug report text outperform fine-tuned transformers for fault localization in industrial software using five years of ABB Robotics data.
LLM-powered monitoring of UI similarity allows random testing tools to escape tarpits, yielding 45-55% higher coverage and more unique bugs across 12 apps.
Agent-generated tests mainly act as observational feedback channels and do not meaningfully improve issue resolution success in current LLM software engineering agents.
citing papers explorer
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From Exploration to Specification: LLM-Based Property Generation for Mobile App Testing
PropGen automates property generation for Android app testing via LLM synthesis from guided exploration and feedback refinement, yielding 912 valid properties and 25 previously unknown bugs across 12 apps.
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Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics
Traditional ML models on bug report text outperform fine-tuned transformers for fault localization in industrial software using five years of ABB Robotics data.
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Improving Random Testing via LLM-powered UI Tarpit Escaping for Mobile Apps
LLM-powered monitoring of UI similarity allows random testing tools to escape tarpits, yielding 45-55% higher coverage and more unique bugs across 12 apps.
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Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents
Agent-generated tests mainly act as observational feedback channels and do not meaningfully improve issue resolution success in current LLM software engineering agents.