Assessing and Detecting Components Suffering from Sub-optimal Performance for a Web-based CRM System

评估和检测基于 Web 的 CRM 系统性能不佳的组件

• 批准号: 530840-2018
• 负责人: Jiang, ZhenMing
• 金额: $ 1.82万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=654677
• 关键词: Assessing; Detecting; Components; Suffering; Sub

Performance is one of the crucial factors related to the success and the sustainability of software systems.**Failure to provide satisfactory performance would result in customers' abandonment and loss of revenue. For**example, Amazon reported that one second delay in loading their webpages could result in $1.6 billion loss in**their sales revenue annually. However, it is very challenging to find areas to improve and optimize system**performance due to limitations of existing tools. Instead of directly pin-pointing the sub-optimal components,**existing performance profiling tools can only provide resource or timing information for each individual**component. As components consume processing time and resources while executing the actual tasks, they**might not be the areas suffering from sub-optimal performance. More sophisticated and in-depth analysis is**required to detect potential areas for performance improvement.**Sparky is a web-based software program responsible for CRM (Customer Relation Management) and quoting**for high speed ink-jet printer jobs for Copywell. Existing process is complex and time consuming. Improve the**efficiency and scalability of Sparky would greatly increase the amount of tasks that can be handled, and hence**results in an increase in the Copywell's income. We process a three-step process to evaluate and improve the**architecture of Sparky. First, we will characterize the workload of Sparky by surveying the domain experts and**mining the existing historical usage data. Second, we will assess Sparky's performance by executing**performance tests and building performance models. Third, various approaches (architectural-level analysis,**design/code-level analysis, and performance anti-pattern detection) will be applied to detect sub-optimal**performance components based on the resulting models.

性能是与软件系统的成功和可持续性有关的关键因素之一。**未能提供令人满意的性能将导致客户放弃和收入损失。例如，亚马逊报告说，加载其网页的一秒钟延迟可能会导致每年16亿美元的销售收入损失。但是，由于现有工具的限制，寻找改进和优化系统性能的领域是非常具有挑战性的。 **现有的性能分析工具无需直接固定子最佳组件，只能为每个单独的**组件提供资源或计时信息。由于组件在执行实际任务时会消耗处理时间和资源，因此它们可能并不是遭受次优性能的领域。需要更复杂和深入的分析**才能检测潜在的性能改进领域。现有过程很复杂且耗时。提高Sparky的**效率和可扩展性将大大增加可以处理的任务数量，从而导致复制Well的收入增加。我们处理一个三步的过程，以评估和改善Sparky的**体系结构。首先，我们将通过调查域专家进行调查并**挖掘现有的历史用法数据来表征Sparky的工作量。其次，我们将通过执行**绩效测试和建筑绩效模型来评估Sparky的性能。第三，将采用各种方法（建筑级分析，**设计/代码级分析和性能反图案检测），以根据所得模型来检测次优**性能组件。