Preetha Chatterjee, Manziba Akanda Nishi, Kostadin Damevski, Vinay Augustine, L. Pollock, Nicholas A. Kraft
{"title":"What information about code snippets is available in different software-related documents? An exploratory study","authors":"Preetha Chatterjee, Manziba Akanda Nishi, Kostadin Damevski, Vinay Augustine, L. Pollock, Nicholas A. Kraft","doi":"10.1109/SANER.2017.7884638","DOIUrl":null,"url":null,"abstract":"A large corpora of software-related documents is available on the Web, and these documents offer the unique opportunity to learn from what developers are saying or asking about the code snippets that they are discussing. For example, the natural language in a bug report provides information about what is not functioning properly in a particular code snippet. Previous research has mined information about code snippets from bug reports, emails, and Q&A forums. This paper describes an exploratory study into the kinds of information that is embedded in different software-related documents. The goal of the study is to gain insight into the potential value and difficulty of mining the natural language text associated with the code snippets found in a variety of software-related documents, including blog posts, API documentation, code reviews, and public chats.","PeriodicalId":6541,"journal":{"name":"2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"87 1","pages":"382-386"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SANER.2017.7884638","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
A large corpora of software-related documents is available on the Web, and these documents offer the unique opportunity to learn from what developers are saying or asking about the code snippets that they are discussing. For example, the natural language in a bug report provides information about what is not functioning properly in a particular code snippet. Previous research has mined information about code snippets from bug reports, emails, and Q&A forums. This paper describes an exploratory study into the kinds of information that is embedded in different software-related documents. The goal of the study is to gain insight into the potential value and difficulty of mining the natural language text associated with the code snippets found in a variety of software-related documents, including blog posts, API documentation, code reviews, and public chats.