文章基本信息

标题：Big Code: New Opportunities for Improving Software Construction
本地全文：下载
作者：Francisco Ortin ; Javier Escalada ; Oscar Rodriguez-Prieto 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2016
卷号：11
期号：11
页码：1083-1008
DOI：10.17706/jsw.11.11.1083-1088
出版社：Academy Publisher
摘要：An emerging research topic called big code has recently appeared. Big code is based on the idea that open source code repositories can be used to create new kind of programming tools and services to improve software reliability and construction. We discuss different fields of application of big code, and the key issues to implement tools aimed at improving software construction following this approach. We describe the existing works that have already used this idea to build tools for vulnerability detection, software deobfuscation, automatic code completion for API usage, and efficient querying using detailed source-code information. Then, we propose different fields of application and the key issues found. We identify eight different fields where big code may be applied, and describe different examples for each field. We also detect seven different issues that must be tackled when creating tools based on the big code approach.
其他关键词：Big code, graph database, machine learning, probabilistic reasoning, program analysis