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

性能是与软件系统的成功和可持续性相关的关键因素之一。未能提供令人满意的表现将导致客户放弃及收入损失。例如，亚马逊报告说，加载网页的一秒钟延迟可能导致其每年销售收入损失16亿美元。然而，由于现有工具的局限性，找到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不能直接指出次优组件，而只能为每个单独的 ** 组件提供资源或时序信息。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 ** 可能不是性能次优的区域。** 需要进行更复杂和深入的分析，以发现可能改进业绩的领域。Sparky是一个基于网络的软件程序，负责CRM（客户关系管理）和报价 ** 为高速喷墨打印机工作的Copywell。现有流程复杂且耗时。提高Sparky的 ** 效率和可扩展性将大大增加可以处理的任务量，因此 ** 会增加Copywell的收入。我们通过三个步骤来评估和改进Sparky的 ** 架构。首先，我们将通过调查领域专家和 ** 挖掘现有的历史使用数据来描述Sparky的工作负载。其次，我们将通过执行 ** 性能测试和构建性能模型来评估Sparky的性能。第三，各种方法（架构级分析，** 设计/代码级分析，和性能反模式检测）将被应用于检测次优 ** 性能组件的基础上产生的模型。

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.