Proactive performance assurance in Mobeewave****

Mobeewave 中的主动性能保证****

• 批准号: 534036-2018
• 负责人: Shang, Weiyi
• 金额: $ 1.82万
• 依托单位: Concordia 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=664861
• 关键词: Proactive; performance; assurance; Mobeewave

Mobeewave offers their award-winning services to tackle the challenges of mobile payment with their software solutions of in-person peer-to-peer and micro-merchant transactions. With their software solutions, it possible for anyone to accept money using their phones' NFC capability. Due to the strong need of Mobeewave's software solution, and the criticality of mobile payment solution, performance has become an detrimental factor to the success of Mobeewave. Our goal is to to design automated approaches that can proactively predict and detect performance issues. The proposed work intersects two areas of software engineering: software performance and software quality. Prior research proposes to monitor and improve software performance with various automated techniques. However, identifying performance issues remains a task that is conducted after fact, i.e., after the system is built and deployed in the field or dedicated performance testing environments. Large amounts of resources are required to detect, locate, understand and fix performance regressions at such a late stage in the development circle. We plan to leverage techniques that are based on machine learning and mining software repositories that provides just-in-time prediction and localization on the software changes that may introduce negative impact on system performance. The outcome of this work will advance the state- of-the-art in performance assurance activities in the area of software engineering in general and provide Mobeewave with an advanced infrastructure to support their performance assurance activities.**

Mobeewave提供他们屡获殊荣的服务，以应对移动的支付的挑战与他们的软件解决方案的人点对点和微型商家交易。通过他们的软件解决方案，任何人都可以使用手机的NFC功能接受金钱。由于对Mobeewave软件解决方案的强烈需求，以及移动的支付解决方案的关键性，性能已成为Mobeewave成功的不利因素。我们的目标是设计能够主动预测和检测性能问题的自动化方法。拟议的工作交叉软件工程的两个领域：软件性能和软件质量。先前的研究提出了各种自动化技术来监控和提高软件性能。然而，确定业绩问题仍然是一项事后进行的任务，即，在系统构建并部署在现场或专用性能测试环境中之后。在开发周期的后期阶段，需要大量的资源来检测、定位、理解和修复性能退化。我们计划利用基于机器学习和挖掘软件存储库的技术，对可能对系统性能产生负面影响的软件更改进行及时预测和本地化。这项工作的成果将推动软件工程领域性能保证活动的发展，并为Mobeewave提供先进的基础设施，以支持其性能保证活动。