LLM-Enhanced Explanatory Signals for Improved API Recommendation in Consumer Electronics Software Development

The development of modern consumer electronics (CE), from smart home ecosystems to wearable devices, hinges on integrating a diverse array of APIs from heterogeneous hardware and cloud services. Existing recommendation methods can generate candidate API lists but lack explanatory power. As a result, developers must interpret the semantic role of each API and its relevance to high-level requirements. This manual process creates a bottleneck and delays feature deployment. To address this limitation, we propose a novel Explanatory Signal-driven API Recommendation framework for CE software development, named ESAR. ESAR leverages Large Language Models (LLMs) to generate recommendation explanations, which are clear and structured rationales that explicitly describe how each recommended API contributes to the developer’s goal. Specifically, ESAR first adopts an LLM-based data augmentation approach to synthesize explanatory signals that explicitly encode the semantic relevance between APIs and development requirements. It then incorporates these signals into the recommendation model through a three-stage progressive training strategy, enabling the model to provide not only accurate API recommendations but also actionable explanations that facilitate rapid validation and enhance developer trust. Experiments conducted on a large-scale dataset from ProgrammableWeb, whose complex API integration patterns are highly representative of the challenges in modern cloud-connected CE software, show that ESAR achieves significant improvements on NDCG metrics, while maintaining comparable performance to strong baselines on MAP, and generates explanations with greater textual diversity and higher semantic consistency with development requirements.