Christian D. Newman, Natalia Dragan, M. Collard, Jonathan I. Maletic, M. J. Decker, Drew T. Guarnera, Nahla J. Abid
{"title":"Automatically Generating Natural Language Documentation for Methods","authors":"Christian D. Newman, Natalia Dragan, M. Collard, Jonathan I. Maletic, M. J. Decker, Drew T. Guarnera, Nahla J. Abid","doi":"10.1109/DySDoc3.2018.00007","DOIUrl":null,"url":null,"abstract":"A tool to automatically generate natural language documentation summaries for methods is presented. The approach uses prior work by the authors on stereotyping methods along with the source code analysis framework srcML. First, each method is automatically assigned a stereotype(s) based on static analysis and a set of heuristics. Then, the approach uses the stereotype information, static analysis, and predefined templates to generate a natural-language summary for each method. This summary is automatically added to the code base as a comment for each method. The predefined templates are designed to produce a generic summary for specific method stereotypes.","PeriodicalId":375729,"journal":{"name":"2018 IEEE Third International Workshop on Dynamic Software Documentation (DySDoc3)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Third International Workshop on Dynamic Software Documentation (DySDoc3)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DySDoc3.2018.00007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
A tool to automatically generate natural language documentation summaries for methods is presented. The approach uses prior work by the authors on stereotyping methods along with the source code analysis framework srcML. First, each method is automatically assigned a stereotype(s) based on static analysis and a set of heuristics. Then, the approach uses the stereotype information, static analysis, and predefined templates to generate a natural-language summary for each method. This summary is automatically added to the code base as a comment for each method. The predefined templates are designed to produce a generic summary for specific method stereotypes.