ALADDIN is a user-requirement-driven GUI test generation framework that incrementally navigates mobile app UIs and builds LLM-guided oracles to validate both correct and faulty user-requested functionalities across six apps.
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Introduces contextualized code pretraining with caller-callee pairs from static analysis to train CallerGen models that outperform baselines on the new CallerEval benchmark.
Hallucination Inspector verifies symbols in LLM-generated API migration code against a documentation-derived knowledge base using AST extraction, identifying scaffolding hallucinations and cutting false positives versus standard metrics in preliminary Android tests.
An empirical study of real-world issues yields a taxonomy of 34 fault types, symptoms, and root causes in agentic AI systems, validated by 145 practitioners.
An AI-native TDD framework operationalizes classical TDD principles as prompt-level and workflow-level governance mechanisms in a layered multi-agent architecture to improve stability and reproducibility of LLM code generation.
citing papers explorer
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Automated Functional Testing for Malleable Mobile Application Driven from User Intent
ALADDIN is a user-requirement-driven GUI test generation framework that incrementally navigates mobile app UIs and builds LLM-guided oracles to validate both correct and faulty user-requested functionalities across six apps.
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Contextualized Code Pretraining for Code Generation
Introduces contextualized code pretraining with caller-callee pairs from static analysis to train CallerGen models that outperform baselines on the new CallerEval benchmark.
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Hallucination Inspector: A Fact-Checking Judge for API Migration
Hallucination Inspector verifies symbols in LLM-generated API migration code against a documentation-derived knowledge base using AST extraction, identifying scaffolding hallucinations and cutting false positives versus standard metrics in preliminary Android tests.
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Characterizing Faults in Agentic AI: A Taxonomy of Types, Symptoms, and Root Causes
An empirical study of real-world issues yields a taxonomy of 34 fault types, symptoms, and root causes in agentic AI systems, validated by 145 practitioners.
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TDD Governance for Multi-Agent Code Generation via Prompt Engineering
An AI-native TDD framework operationalizes classical TDD principles as prompt-level and workflow-level governance mechanisms in a layered multi-agent architecture to improve stability and reproducibility of LLM code generation.