Test case prioritization

该提案是 IBM CAS Canada 和瑞尔森大学数据科学实验室之间的合作成果，旨在构建软件工具辅助的测试用例优先级模型。软件分析指导从业者在整个软件开发过程中做出决策。在这种情况下，预测模型可以帮助管理者通过以智能且有意义的方式分析现有项目数据的模式来有效地组织资源并识别问题。有必要拥有一种一体化工具来管理数据收集、分析和校准，并与软件组织中的现有系统进行通信以提供有价值的信息。多年来我们开发了这样一个工具，称为 Dione。 Dione 是一款用于软件交付平台的软件交付智能和预测分析工具。它具有智能决策优化引擎（挖掘、过滤、预测）。它集成了数据提取、报告和预测模块，以实现完整的决策支持周期，并以与平台无关的方式提供最低配置解决方案。在这个项目中，我们的目标是将 Dione 部署到 IBM Rational Team Concert (RTC)，以解决有关测试用例优先级的实际挑战。测试用例优先级技术按顺序安排测试用例执行，以尝试提高其满足某些性能目标的有效性。各种目标都是可能的；其中之一涉及故障检测率——衡量测试过程中检测到故障的速度的指标。测试过程中故障检测率的提高可以为被测系统提供更快的反馈，并让软件工程师比其他方式更早地开始纠正故障。如果所提出的解决方案成功应用，它不仅会影响RTC的产品质量，而且对于RTC的用户来说也是一个最佳实践。换句话说，这将为加拿大的特定软件行业带来级联效益。

This proposal is a collaborative effort between IBM CAS Canada and the Data Science Laboratory at Ryerson University to build a software tool-aided test case prioritization model. Software analytics guide practitioners in decision making throughout the software development process. In this context, prediction models can help managers efficiently organize their resources and identify problems by analyzing patterns on existing project data in an intelligent and meaningful manner. It is necessary to have all-in-one tools that manage data collection, analysis, and calibration as well as communicate with existing systems in software organizations to provide valuable information. Over the years we have developed such a tool, called Dione. Dione is a software delivery intelligence and predictive analytics tool for a software delivery platform. It has an intelligent decision optimization engine (mining, filtering, prediction). It integrates data extraction, reporting, and prediction modules for a complete decision support cycle and offers a minimum configuration solution in a platform agnostic manner. In this project we aim to deploy Dione to IBM Rational Team Concert (RTC) to address practical challenges regarding Test Case prioritization. Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness in meeting some performance goal. Various goals are possible; one involves rate of fault detection - a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test, and let software engineers begin correcting faults earlier than might otherwise be possible. In the case of successful application of the proposed solution, it will not only impact the product quality of RTC but also be a best practice for the users of it. In other words, this will trigger cascaded benefits for a particular software industry in Canada.