API Misuse Detection Method Based on Transformer

Software developers need to take advantage of a variety of APIs (application programming interface) in their programs to implement specific functions. The problem of API misuses often arises when developers have incorrect understandings about the new APIs without carefully reading API documents. In order to avoid software defects caused by API misuse, researchers have explored multiple methods, including using AI(artificial intelligence) technology.As a kind of neural network in AI, Transformer has a good sequence processing ability, and the self attention mechanism used by Transformer can better catch the relation in a sequence or between different sequences. Besides it has a good model interpretability. From the perspective of combining API misuse detection with AI, this paper implements a standard Transformer model and a target-combination Transformer model to the learning of API usage information in a named API call sequence extracted from API usage program code. Then we present in the paper the way that our models use API usage information to detect if an API is misused in code. We use F1, precision and recall to evaluate the detection ability and show the advantages of our models in these three indexes. Besides, our models based on Transformer both have a better convergence. Finally, this paper explains why the models based on Transformer has a better performance by showing attention weight among different elements in code.