When searching code, developers may express additional constraints (e.g., functional constraints and nonfunctional constraints) on the implementations of desired functionalities in the queries. Existing code search tools treat the queries as a whole and ignore the different implications of different parts of the queries. Moreover, these tools usually return a ranked list of candidate code snippets without any explanations. Therefore, the developers often find it hard to choose the desired results and build confidence on them. In this article, we conduct a developer survey to better understand and address these issues and induct some insights from the survey results. Based on the insights, we propose XCoS, an explainable code search approach based on query scoping and knowledge graph. XCoS extracts a background knowledge graph from general knowledge bases like Wikidata and Wikipedia. Given a code search query, XCoS identifies different parts (i.e., functionalities, functional constraints, nonfunctional constraints) from it and use the expressions of functionalities and functional constraints to search the codebase. It then links both the query and the candidate code snippets to the concepts in the background knowledge graph and generates explanations based on the association paths between these two parts of concepts together with relevant descriptions. XCoS uses an interactive user interface that allows the user to better understand the associations between candidate code snippets and the query from different aspects and choose the desired results. Our evaluation shows that the quality of the extracted background knowledge and the concept linkings in codebase is generally high. Furthermore, the generated explanations are considered complete, concise, and readable, and the approach can help developers find the desired code snippets more accurately and confidently.

When reusing or modifying code, developers need to understand the implicit knowledge behind a piece of code in addition to the literal meaning of code. Such implicit knowledge involves related concepts and their explanations. Uncovering and understanding the implicit knowledge in code are challenging due to the extensive use of abbreviations, scattered expressions of concepts, and ambiguity of concept mentions. In this paper, we propose an automatic approach (called CoLiCo) that can uncover implicit concepts in code and link the uncovered concepts to Wikipedia. Based on a trained identifier embedding model, CoLiCo identifies Wikipedia concepts mentioned in a given code snippet and excerpts a paragraph-level explanation from Wikipedia for each concept. During the process, CoLiCo resolves identifier abbreviation (i.e., concepts mentioned in the form of abbreviations) and identifier aggregation (i.e., concepts mentioned by an aggregation of multiple identifiers) based on identifier embedding and mining of identifier abbreviation/aggregation relations. Experimental study shows that CoLiCo outperforms a general entity linking approach by 38.7% in the correctness of concept linking and identifies 96.7% more correct concept linkings on a dataset with 629 code snippets. The concept linking is significant for program understanding in 54% code snippets. Our user study shows that CoLiCo can significantly shorten the time and improve the correctness in code comprehension tasks that intensively involve implicit knowledge.

In this paper, we present OpenAPIDocGen2, a tool that generates on-demand class documentation based on source code and documentation analysis. For a given class, OpenAPIDocGen2 generates a combined documentation for it, which includes functionality descriptions, directives, domain concepts, usage examples, class/method roles, key methods, relevant classes/methods, characteristics and concepts classification, and usage scenarios.