Abstract In software Q&A sites, there are many code‐solving examples of individual program problems, and these codes with explanatory natural language descriptions are easy to understand and reuse. Code search in software Q&A sites increases the productivity of developers. However, previous approaches to code search fail to capture structural code information and the interactivity between source codes and natural queries. In other words, most of them focus on specific code structures only. This paper proposes TCS (Transformer‐based code search), a novel neural network, to catch structural information for searching valid source codes from the query, which is vital for code search. The multi‐head attention mechanism in Transformer helps TCS learn enough information about the underlying semantic vector representation of codes and queries. An aligned attention matrix is also employed to catch relationships between codes and queries. Experimental results show that the proposed TCS can learn more structural information and has better performance than existing models.

We propose a novel two-stage approach, Stir, for inferring types in incomplete programs that may be ill-formed, where whole-program syntactic analysis often fails. In the first stage, Stir predicts a type tag for each token by using neural networks, and consequently, infers all the simple types in the program. In the second stage, Stir refines the complex types for the tokens with predicted complex type tags. Unlike existing machine-learning-based approaches, which solve type inference as a classification problem, Stir reduces it to a sequence-to-graph parsing problem. According to our experimental results, Stir achieves an accuracy of 97.37 % for simple types. By representing complex types as directed graphs (type graphs), Stir achieves a type similarity score of 77.36 % and 59.61 % for complex types and zero-shot complex types, respectively.