Integrating User Sentiment into Software Evolution Processes

由于用户基数大，需求变化快，因此要保持大规模软件应用程序的质量是一项挑战。源代码重构、面向对象的重构和缺陷预测方法是随着应用程序的发展而不断改进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，感知质量捕捉用户的感知和情绪（例如，用户的态度、意见或感觉）以及实际应用的不完善性。例如，具有很少展示的30个缺陷的应用可以具有比具有频繁展示的一个缺陷的应用更好的感知质量。 不同类型的用户研究，例如，直接观察和实验室方法经常用于研究用户对应用程序质量的感知。然而，这类研究的操作成本很高，而且很难解释不同用户群体的主观结果。 随着诸如社交媒体和社交网络之类的各种社交渠道的激增，在过去十年中，用户更喜欢通过对用户来说更直观的社交渠道提供关于应用的即时反馈，而不是提交缺陷的问题报告。对于具有大的用户基础的应用，可以通过社交渠道（例如，Twitter和Stack Overflow）。这样的用户反馈可以揭示在自然使用环境下发生的更广泛的有问题的使用场景。在这项拟议的研究计划中，我们有兴趣通过分析各种社交渠道中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。我们专注于两个主题：（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;以及（2）利用感知质量，允许从业者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。 研究的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，品牌声誉和最终公司收入。这项研究将直接有利于加拿大一些顶级IT公司的软件演进过程，例如，IBM和黑莓慷慨地为我们提供了他们的数据集和软件来进行我们的研究。拟议的研究计划将培训6名HQP（即，3博士和3硕士）在一个非常重要的加拿大的经济领域。

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.