Most defects in mobile applications are visually observable on the device screen. Since automated mechanisms for detecting and reporting such defects are often unavailable, users, testers, and developers must manually submit bug reports. However, these reports are frequently incomplete, ambiguous, or inaccurate, often lacking the information needed to understand, reproduce, and diagnose defects. We formulate automatic bug report enhancement as the problem of connecting user-written bug reports with application execution. We present BUGSCRIBE, an LLM-powered approach that links bug report information with app-specific UI execution information to infer and generate accurate, complete, and correct Observed Behavior (OB), Expected Behavior (EB), and Steps to Reproduce (S2Rs). To support BUGSCRIBE's design and evaluation, we develop a bug report quality model and use it to identify the most effective context for each component. We evaluate BUGSCRIBE on 48 bug reports from 26 Android applications with manually constructed ground truth. Our results show that BUGSCRIBE generates higher-quality bug report components than the original reports and three LLM-based baselines, improving S2R quality by 44.2%--84.7%, and OB/EB quality by up to 35.2%. We further show that BUGSCRIBE-generated reports help an LLM-based agent reproduce Android bugs, improving the reproduction rate by 28.1%--57.7% compared to the original reports and the baselines.

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