Integrating User Sentiment into Software Evolution Processes

维护具有大量用户群和快速变化的需求的大型软件应用程序的质量具有挑战性。源代码重组、面向对象的重构和缺陷预测方法体现了在应用程序发展过程中提高应用程序质量的积极研究工作。然而，应用程序的质量并不总是反映该应用程序的感知质量。具体来说，感知质量捕获用户对应用程序的感知和情绪（例如，用户的态度、意见或感觉）以及实际应用程序的缺陷。例如，具有 30 个很少出现的缺陷的应用程序可能比具有 1 个经常出现的缺陷的应用程序具有更好的感知质量。 不同类型的用户研究，例如直接观察和实验室方法，通常用于研究用户对应用程序质量的看法。然而，此类研究的运营成本很高，而且很难解释不同用户群体的主观结果。 随着过去十年中社交媒体和社交网络等各种社交渠道的激增，用户更愿意通过对用户来说更直观的社交渠道提供对应用程序的即时反馈，而不是针对缺陷提交问题报告。对于拥有大量用户群的应用程序，可以通过社交渠道（例如 Twitter 和 Stack Overflow）收集大量用户反馈。此类用户反馈可以揭示自然使用环境下发生的更广泛的有问题的使用场景。在这个拟议的研究计划中，我们感兴趣的是通过分析各种社交渠道中可用的大量众包数据来捕获应用程序的感知质量，并利用这些信息来提高应用程序的感知质量。我们关注两个主题：（1）将感知质量集成到缺陷修复过程中，以帮助从业者优先考虑对感知质量影响最大的缺陷，并为开发人员提供对不利用户情绪的即时响应； (2)利用感知质量使从业者能够快速调整其应用程序以适应不断发展的软件组件，并帮助从业者选择具有高感知质量的组件进行软件集成。 研究结果可以帮助从业者专注于高影响力的问题，从而提高客户满意度、品牌声誉并最终提高公司收入。这项研究将直接有利于加拿大一些顶级 IT 公司的软件演化过程，例如 IBM 和 Blackberry，它们慷慨地为我们提供了访问其数据集和软件的权限，以开展我们的研究。拟议的研究计划将在对加拿大经济非常重要的领域培训 6 名高级人才（即 3 名博士和 3 名硕士）。

It is challenging to maintain the quality of large-scale software applications with a large user base and rapidly evolving requirements. Source code restructuring, object oriented refactoring and defect prediction approaches exemplify active research efforts to improve the quality of applications as they evolve. However, the quality of an application does not always reflect perceived quality of that application. Specifically, perceived quality captures users' perception and sentiment (e.g., users' attitude, opinion or feeling) towards an application and the faultiness of the actual application. For example, an application with 30 defects which are rarely exhibited may have better perceived quality than an application with one defect that is exhibited frequently. Different types of user studies, e.g., direct observation and laboratory methods, are often used to study users' perception of the quality of an application. However, such types of studies are costly to operate, and it is hard to interpret the subjective results across different user groups. As the proliferation of various social channels, such as social media and social networks, has shot up during the past decade, users prefer to provide instant feedback on an application through social channels which are more intuitive for users, instead of submitting an issue report for a defect. For applications with a large user base, a large amount of user feedback can be collected over social channels (e.g., Twitter and Stack Overflow). Such user feedback can uncover a wider range of problematic usage scenarios which occur under natural usage environments. In this proposed research program, we are interested in capturing perceived quality of applications through analyzing the enormous amount of crowdsourced data available across various social channels and leveraging such information to improve perceived quality of applications as they evolve. We focus on two themes: (1) Integrating perceived quality into the defect fixing process to help practitioners prioritize defects with the highest impact on perceived quality and to provide instant developer response to unfavorable user sentiment; and (2) Leveraging perceived quality to allow practitioners to quickly adapt their applications to the evolving software components and help practitioners select components with high perceived quality for software integration. The result of the research can help practitioners focus on high impact issues, thereby increasing customer satisfaction, brand reputation and ultimately company revenues. The research will directly benefit the software evolution processes at some of Canada's top IT companies, e.g., IBM and Blackberry who are generously providing us with access to their datasets and software to conduct our research. The proposed research program will train 6 HQPs (i.e., 3 PhD and 3 MSc) in an area of great importance to Canada's economy.