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）必须科普规模问题，这些问题使传统软件所面临的问题相形见绌。这些部署复杂、运营成本高的系统每天必须管理数十亿笔交易，而不会出现停机。然而，与这些系统相关的重要决策往往基于直觉和猜测。这种随意的决定导致资源浪费和成本增加。拟议的软件分析CRC将通过挖掘与此类系统相关的丰富但很少利用的存储库（例如，操作日志和开发历史）。