Describing the APIs comprehensively: Obtaining the holistic representations from multiple modalities data for different tasks

Context: API (Application Programming Interface) is an important object in software development, and describing them properly is the basis for solving related problems, such as API recommendation. Recently, multimodal data fusing approaches become a hot research topic in different fields, and they can be used to get comprehensive representations of things by describing them from different angles. This provides us with a new useful way for API representation. Objective: In this work, we aim at describing APIs comprehensively by fusing information from multimodal data for supporting different API-related tasks.Method: To achieve this goal, we propose a novel approach BDBM (Bimodal Deep Boltzmann Machine) to obtain holistic representations of APIs by fusing the information in text and code modalities, which are the API descriptions and the codes of the products. Then, the BDBM is applied to two typical API tasks (API recommendation and similar API mining) to analyze its performance. Results and Conclusion: The results show that the API recommendation based on BDBM outperforms the ones based on unimodal API information, our method's precisions can reach 0.67, 0.65, 0.61 at top-3, top-5 and top-10, while MAP and MRR are 0.66 and 0.67. Meanwhile, the close representations give similar APIs with similar functionalities as well as similar usage in codes. Thus, we believe that multimodal data fusion is suitable for describing APIs, and the holistic representations given by BDBM can be used in different API-related tasks.