arXiv:2605.16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry. These approaches embed natural-language queries and code snippets into a shared embedding space and retrieve results based on vector similarity. Despit strong performance on benchmark datasets, they often suffer from poor explainability and generalization. Retrieved code may appear semantically similar yet miss critical functional requirements of the query, while providing no explanation of why the result was retrieved. Moreover, such failures become more severe
The increasing adoption of semantic code search in academia and industry has highlighted its current limitations in explainability and generalization, driving the need for more robust solutions like XSearch.
This development addresses critical shortcomings in AI-driven code tools, enhancing trust and effectiveness by enabling developers to understand why specific code results are retrieved and improving the reliability of autonomous software development.
Code search tools will evolve from black-box similarity matching to explainable systems, providing clearer justifications for results and improving their practical utility in complex development scenarios.
- · Software developers
- · Companies using AI for code generation/search
- · AI agents in software development
- · Black-box semantic search providers
- · Less transparent AI code tools
Improved reliability and explainability of AI-assisted code development workflows.
Accelerated adoption of AI agents in software engineering as transparency and trustworthiness increase.
Potential for new regulations or standards around explainability in AI systems for critical software infrastructure.
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Read at arXiv cs.AI