Software Analytics

软件分析

• 批准号: CRC-2018-00011
• 负责人: Hassan, Ahmed
• 金额: $ 7.29万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=730287
• 关键词: Software; Analytics

Ultra-large scale (ULS) systems (e.g., nation-wide telephony and banking infrastructures) must cope with problems of scale that dwarf the ones facing traditional software. These massively-deployed costly-to-operate systems must manage billions of transactions daily with no downtime. Yet, important decisions associated with such systems are often based on intuition and guesswork. Such haphazard decisions lead to wasted resources and increased costs. The proposed CRC on Software Analytics will change the Industry's status quo by developing fact-based decision support approaches for such systems through the mining of the rich yet-rarely leverage repositories associated with such systems (e.g., operational logs and development history).

超大规模(ULS)系统(例如，全国性的电话和银行基础设施)必须处理使传统软件所面临的问题相形见绌的规模问题。这些大规模部署、操作成本高昂的系统必须在不停机的情况下每天管理数十亿笔交易。然而，与这类系统相关的重要决策往往是基于直觉和猜测。这种随意的决定会导致资源浪费和成本增加。拟议的关于软件分析的CRC将通过挖掘丰富但很少利用与这类系统相关的存储库(例如，操作日志和开发历史)，为这类系统开发基于事实的决策支持方法，从而改变行业的现状。