Based on developer needs and usage scenarios, API (Application Programming Interface) recommendation is the process of assisting developers in finding the required API among numerous candidate APIs. Previous studies mainly modeled API recommendation as the recommendation task, which can recommend multiple candidate APIs for the given query, and developers may not yet be able to find what they need. Motivated by the neural machine translation research domain, we can model this problem as the generation task, which aims to directly generate the required API for the developer query. After our preliminary investigation, we find the performance of this intuitive approach is not promising. The reason is that there exists an error when generating the prefixes of the API. However, developers may know certain API prefix information during actual development in most cases. Therefore, we model this problem as the automatic completion task and propose a novel approach APICom based on prompt learning, which can generate API related to the query according to the prompts (i.e., API prefix information). Moreover, the effectiveness of APICom highly depends on the quality of the training dataset. In this study, we further design a novel gradient-based adversarial training method ATCom for data augmentation, which can improve the normalized stability when generating adversarial examples. To evaluate the effectiveness of APICom, we consider a corpus of 33k developer queries and corresponding APIs. Compared with the state-of-the-art baselines, our experimental results show that APICom can outperform all baselines by at least 40.02%, 13.20%, and 16.31% in terms of the performance measures EM@1, MRR, and MAP. Finally, our ablation studies confirm the effectiveness of our component setting (such as our designed adversarial training method, our used pre-trained model, and prompt learning) in APICom.

The code comment generation task is designed for developers to understand programs more quickly during development and maintenance.However, the existing automatic code comment generation models can not generate valuable comments for developers.It is necessary to explore a technology that can optimize the performance of code comment generation models without changing the model.We consider adversarial training as the experimental object, which can improve the robustness and generalization of the model.We present a large-scale study to experimentally validate the performance of gradient-based adversarial training methods in the code comment generation task.The results show that adversarial training can improve the model performance by generating adversarial examples without changing the model.Our empirical study can provide a new perspective for researchers to improve the performance of code comment generation models.

Fault localization techniques have been developed for decades. Spectrum Based Fault Localization (SBFL) is a popular strategy in this research topic. However, SBFL is well known for low accuracy, mainly due to simply using a coverage matrix of program executions. In this paper, we propose a method based on graph neural network (AGFL), characterized by the adjacent matrix of the abstract syntax tree and the word vector of each program token. Referring to the Dstar, we calculate the suspiciousness of the statements and rank these statements. The experiment carried on Defects4J, a widely used benchmark, reveals that AGFL can locate 178 of the 262 studied bugs within Top-1, while state-of-the-art techniques at most locate 148 within Top-1. We also investigate the impacts of hyper-parameters (e.g., epoch and learning rate). The results show that AGFL has the best effect when the epoch is 100 and the learning rate is 0.0001. This value of epoch and learning rate increases by 66% compared to the worst on Top-1.