CAREER: Combating Performance Bugs in Software Systems

职业：对抗软件系统中的性能错误

• 批准号: 1514189
• 负责人: Shan Lu
• 金额: $ 23.89万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514189&HistoricalAwards=false
• 关键词: CAREER; Combating; Performance; Bugs; Software

Performance bugs cause unintended performance degradation and energy waste in mainstream software. These bugs can easily slow down production software for several times and are posing increasing threat to software quality with the meager increases in single threaded performance in multi-core era and an increasing emphasis on energy efficiency.Previous works for performance bugs, including testing and profiling, mostly treat software as a black-box, and are usually too late or ineffective in preventing the damage of performance bugs. This NSF CAREER research project seeks to develop systems and tools that take a white-box approach to addressing the open problem of performance bugs. First, it conducts an empirical study to enrich the understanding of performance bugs in real-world. Second, it develops tools that can automatically detect performance bugs before, during, and after bug manifestation. Third, it designs a testing framework to expose performance bugs before software release. Finally, it also looks at the performance bug issue in multi-threaded programs.The education focus of this project is to broaden the software curriculum to bring students more awareness and exercise of performance and correctness issues in software, especially multi-threaded software.This research will improve the understanding of performance waste problem in software, provide substantial tool support to help lower software development and performance testing costs, and improve software users' everyday experience through faster software. It will also help improve the energy efficiency of production-run software and protect environment in the field.

性能错误会导致主流软件意外的性能下降和能源浪费。这些错误很容易使生产软件速度减慢数倍，并且随着多核时代单线程性能的微弱提高以及对能源效率的日益重视，对软件质量构成越来越大的威胁。以前针对性能错误的工作，包括测试和分析，大多数将软件视为黑匣子，并且通常在防止性能错误的损害方面为时已晚或无效。这个 NSF 职业研究项目旨在开发采用白盒方法来解决性能错误的开放问题的系统和工具。首先，它进行了实证研究，以丰富对现实世界中性能错误的理解。其次，它开发了可以在错误出现之前、期间和之后自动检测性能错误的工具。第三，它设计了一个测试框架，以便在软件发布之前暴露性能错误。最后，还研究了多线程程序中的性能错误问题。该项目的教育重点是拓宽软件课程，让学生对软件，特别是多线程软件中的性能和正确性问题有更多的认识和锻炼。研究将提高对软件性能浪费问题的理解，提供实质性的工具支持，帮助降低软件开发和性能测试成本，并通过更快的软件改善软件用户的日常体验。它还将有助于提高生产运行软件的能源效率并保护现场环境。