LLM-CompDroid: Repairing Configuration Compatibility Bugs in Android Apps with Pre-trained Large Language Models
CoRR(2024)
摘要
XML configurations are integral to the Android development framework,
particularly in the realm of UI display. However, these configurations can
introduce compatibility issues (bugs), resulting in divergent visual outcomes
and system crashes across various Android API versions (levels). In this study,
we systematically investigate LLM-based approaches for detecting and repairing
configuration compatibility bugs. Our findings highlight certain limitations of
LLMs in effectively identifying and resolving these bugs, while also revealing
their potential in addressing complex, hard-to-repair issues that traditional
tools struggle with. Leveraging these insights, we introduce the LLM-CompDroid
framework, which combines the strengths of LLMs and traditional tools for bug
resolution. Our experimental results demonstrate a significant enhancement in
bug resolution performance by LLM-CompDroid, with LLM-CompDroid-GPT-3.5 and
LLM-CompDroid-GPT-4 surpassing the state-of-the-art tool, ConfFix, by at least
9.8
innovative approach holds promise for advancing the reliability and robustness
of Android applications, making a valuable contribution to the field of
software development.
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