Developers often search for application programming interfaces (APIs) and their usage patterns to speed up the efficiency of software development. This paper focuses on the API sequence search task, which refers to using a function-relevant textual query to search for API sequences mined from open-source software repositories that can implement this function. However, the severe semantic gap between text and API makes it challenging to discover the correspondence between natural language queries and desired API sequences. Therefore, we propose a method called documentation-guided API sequence search (DGAS), through which we do not need to worry about the semantic gap between text and API. Specifically, DGAS consists of documentation-guided cross-modal attention (DGCA) and documentation-guided cross-modal matching (DGCM). DGCA calculates the cross-modal attention map using features extracted from the same modality (i.e., API documentation sequence and textual query) instead of from different modalities (i.e., API sequence and textual query) to bridge the semantic gap during the cross-modal attention phase. Besides, DGCM takes API documentation as supplementary information of API sequence to bridge the semantic gap during the cross-modal matching phase. We use the API documentation to extend the existing dataset for API sequence generation to construct a dataset for API sequence search to evaluate DGAS. Experimental results show that DGAS outperforms the baseline methods.

Software cross-modal retrieval is a popular yet challenging direction, such as bug localization and code search. Previous studies generally map natural language texts and codes into a homogeneous semantic space for similarity measurement. However, it is not easy to accurately capture their similar semantics in a homogeneous semantic space due to the semantic gap. Therefore, we propose to map the multi-modal data into heterogeneous semantic spaces to capture their unique semantics. Specifically, we propose a novel software cross-modal retrieval framework named Deep Hypothesis Testing (DeepHT). In DeepHT, to capture the unique semantics of the code’s control flow structure, all control flow paths (CFPs) in the control flow graph are mapped to a CFP sample set in the sample space. Meanwhile, the text is mapped to a CFP correlation distribution in the distribution space to model its correlation with different CFPs. The matching score is calculated according to how well the sample set obeys the distribution using hypothesis testing. The experimental results on two text-to-code retrieval tasks (i.e., bug localization and code search) and two code-to-text retrieval tasks (i.e., vulnerability knowledge retrieval and historical patch retrieval) show that DeepHT outperforms the baseline methods.