CPA-SEL: Better Tools for Software Understanding

CPA-SEL：更好的软件理解工具

• 批准号: 0811610
• 负责人: Brad Myers
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811610&HistoricalAwards=false
• 关键词: CPA; SEL; Better; Tools; Software

TITLE: Betters Tools for Software UnderstandingPI: Brad A. Myers, Carnegie Mellon UniversityUnderstanding software is a prerequisite to taking any action to change it, and this remains expensive and error-prone. Research with programmers in the field has identified significant barriers to understanding. Lab and field studies of usability barriers in understanding and using APIs will result in models of how developers understand the design of the objects. These results will lead to new software tools for API exploration and understanding. Work on enabling people to better understand and fix bugs through new visualizations and interaction techniques will allow them to ask "Why" and "Why Not" questions about their code, with the answer visualizing the responsible code and dataflow. New tools will support understanding code by others during reverse engineering activities, focusing on how data and control can flow through large and complex programs. Using static analysis techniques and a new "WhatTree" visualization will allow programmers to investigate the update paths of their programs, while supporting the programmers in collecting and keeping track of facts and hypotheses about how the program operates. The results will improve programmer success and thus their overall productivity.

标题：更好的软件理解工具：Brad A. Myers，Carnegie Mellon University Universitynderding Software是采取任何措施更改它的先决条件，这仍然昂贵且容易出错。 与该领域的程序员进行的研究确定了重大的理解障碍。 实验室和现场研究理解和使用API​​中的可用性障碍将导致开发人员如何理解对象的设计模型。 这些结果将为API探索和理解提供新的软件工具。 通过新的可视化和互动技术使人们能够更好地理解和修复错误的工作将使他们询问“为什么”和“为什么不”对其代码的问题，答案可视化负责任的代码和数据流。 新工具将支持在反向工程活动期间了解他人的代码，重点关注数据和控制如何流入大型和复杂的程序。 使用静态分析技术和新的“ Whatree”可视化将使程序员能够调查其程序的更新路径，同时支持程序员收集和跟踪有关程序的运作方式的事实和假设。 结果将提高程序员的成功，从而提高其整体生产率。