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Based on developer needs and usage scenarios, API (Application ProgrammingInterface) recommendation is the process of assisting developers in finding therequired API among numerous candidate APIs. Previous studies mainly modeled APIrecommendation as the recommendation task, which can recommend multiplecandidate APIs for the given query, and developers may not yet be able to findwhat they need. Motivated by the neural machine translation research domain, wecan model this problem as the generation task, which aims to directly generatethe required API for the developer query. After our preliminary investigation,we find the performance of this intuitive approach is not promising. The reasonis that there exists an error when generating the prefixes of the API. However,developers may know certain API prefix information during actual development inmost cases. Therefore, we model this problem as the automatic completion taskand propose a novel approach APICom based on prompt learning, which cangenerate API related to the query according to the prompts (i.e., API prefixinformation). Moreover, the effectiveness of APICom highly depends on thequality of the training dataset. In this study, we further design a novelgradient-based adversarial training method {\atpart} for data augmentation,which can improve the normalized stability when generating adversarialexamples. To evaluate the effectiveness of APICom, we consider a corpus of 33kdeveloper queries and corresponding APIs. Compared with the state-of-the-artbaselines, our experimental results show that APICom can outperform allbaselines by at least 40.02\%, 13.20\%, and 16.31\% in terms of the performancemeasures EM@1, MRR, and MAP. Finally, our ablation studies confirm theeffectiveness of our component setting (such as our designed adversarialtraining method, our used pre-trained model, and prompt learning) in APICom.

APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation