Bridging (programming)

Abstract: Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas. To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus. The corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. In this article, we describe the methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task. We hope that CodeSearchNet Challenge encourages researchers and practitioners to study this interesting task further and will host a competition and leaderboard to track the progress on the challenge. We are also keen on extending CodeSearchNet Challenge to more queries and programming languages in the future.

Abstract: The application of information retrieval techniques to search tasks in software engineering is made difficult by the lexical gap between search queries, usually expressed in natural language (eg English), and retrieved documents, usually expressed in code (eg programming languages) This is often the case in bug and feature location, community question answering, or more generally the communication between technical personnel and non-technical stake holders in a software project In this paper, we propose bridging the lexical gap by projecting natural language statements and code snippets as meaning vectors in a shared representation space In the proposed architecture, word embeddings are first trained on API documents, tutorials, and reference documents, and then aggregated in order to estimate semantic similarities between documents Empirical evaluations show that the learned vector space embeddings lead to improvements in a previously explored bug localization task and a newly defined task of linking API documents to computer programming questions

Abstract: Developers often need to search for appropriate APIs for their programming tasks. Although most libraries have API reference documentation, it is not easy to find appropriate APIs due to the lexical gap and knowledge gap between the natural language description of the programming task and the API description in API documentation. Here, the lexical gap refers to the fact that the same semantic meaning can be expressed by different words, and the knowledge gap refers to the fact that API documentation mainly describes API functionality and structure but lacks other types of information like concepts and purposes, which are usually the key information in the task description. In this paper, we propose an API recommendation approach named BIKER (Bi-Information source based KnowledgE Recommendation) to tackle these two gaps. To bridge the lexical gap, BIKER uses word embedding technique to calculate the similarity score between two text descriptions. Inspired by our survey findings that developers incorporate Stack Overflow posts and API documentation for bridging the knowledge gap, BIKER leverages Stack Overflow posts to extract candidate APIs for a program task, and ranks candidate APIs by considering the query’s similarity with both Stack Overflow posts and API documentation. It also summarizes supplementary information (e.g., API description, code examples in Stack Overflow posts) for each API to help developers select the APIs that are most relevant to their tasks. Our evaluation with 413 API-related questions confirms the effectiveness of BIKER for both class- and method-level API recommendation, compared with state-of-the-art baselines. Our user study with 28 Java developers further demonstrates the practicality of BIKER for API search.

Abstract: This article describes a unique educational project that was implemented in the undergraduate study of computer science in 2002. Nesna University College has been using the example of sexual abuse of children in case study teaching in social informatics, in order to create an environment for intrinsically motivated learning. The project also gave the students a unique opportunity to get involved both emotionally and practically in the field of social informatics. The project is run in cooperation with Save the Children Norway and the Norwegian National Crime Squad. Nesna University College has the only computer science program in the world that has sexual abuse of children as the main topic on the curriculum. The computer science students provide both the Save the Children Norway and the National Criminal Investigation Service with reports on various topics such as secure chat, camera phones and possible abuse, Freenet as a tool for sexual abuse, etc. This exceptional cooperation between higher education and public and private organizations in this field makes the project not only unique, but might also be a major factor in both the willingness of students to learn social informatics and their development of skills in the various topics of social informatics. © 2007 Wiley Periodicals, Inc.