Software Innovation for Mass Emergencies

“给客户他们想要的，客户不知道他们想要什么。”(S·乔布斯)然而，软件需求引出的主要来源是用户。假设涉众知道软件应该为他们做什么，研究社区通过分析与软件产品相关或关于软件产品的数据源，开发了有效的需求获取方法。这些来源要么是访谈或集思广益会议的输出，要么是软件文档或用户反馈。受需求工程的数据驱动的愿景的启发，并相信用户无法准确地想象软件可以如何以及何时帮助他们，我们将需求获取的来源从关于软件的文档转移到关于主题、事件或问题的通用(与软件无关)沟通。这些非结构化通信可以是文本的(例如，推文、公开论坛上的讨论)，也可以是非文本的(例如，在Instagram上分享的照片、飓风的视频)。这意味着，如果一群人正在就某一事件进行沟通，本提案中开发的方法可以创新，并提供帮助他们面对事件的软件服务。这可以是任何群体或社区，例如，大规模紧急情况下的受害者或志愿者，患有慢性病(如糖尿病)的人，或特定群体(如老年人或在校学生)。考虑到一个软件不适合所有关注某一主题的人，解决方案不可能是一个产品。相反，我们的方法引入了一系列(系列)软件产品。我们调整和评估我们提出的应对大规模突发事件的方法。移动设备在紧急情况下扮演着更重要的角色，因为它们可能突然成为所有者仅有的资源之一。这对软件工程师来说是一个独特的机会，可以为移动设备配备软件解决方案(也称为移动应用程序)。在我攻读博士学位期间，只有在麦克默里堡大火期间专注于Twitter上的文本交流，我才发现，在现有的任何紧急应用程序中，排名前40位的基本功能中有85%是不可用的。国家和国际媒体报道的研究结果引发了许多合作请求。在这项提议中，我们遵循三个主要目标：-开发使用机器学习从文本和非文本通用通信中推断需求的方法。-开发一种使用数据统计和挖掘方法定义应用程序功能以满足要求的自动化方法。-提供适用于大规模紧急情况的移动应用产品线，通过使用基于超和元启发式搜索的技术提供一组优化的功能。这一跨学科研究建议对人们的日常生活产生了实实在在的影响。我计划资助2名博士、2名硕士和5名本科生或高中生，研究与加拿大社会相关的问题。

"Give the customers what they want, customers don't know what they want."(S. Jobs) Yet, the primary source for software requirement elicitation is users. Assuming that stakeholders know what the software should do for them, the research community developed efficient methods for requirement elicitation through analyzing data sources related to or about software products. These sources are either output of interview or brainstorming sessions, software documents, or user feedback. Inspired by the data-driven vision for requirements engineering and by believing that users cannot precisely imagine how and when software can help them, we shift the source of requirements elicitation from documents about software to general-purpose (and unrelated to software) communication about a topic, event, or a problem. These unstructured communications could be textual (e.g. tweets, discussions in open forums) or non-textual (e.g. photos shared on Instagram, videos of a hurricane). This means, if a group of people are communicating about an event, the methods developed in this proposal can innovate and offer a software service helping them facing the event. This could be any group or community, for example, victims or volunteers in a mass emergency, people suffering a chronic disease (such as diabetes), or specific groups (like senior citizens, or school students). Considering that one software does not fit all the people being concerned with a given topic, the solution cannot be one product. Instead, our methods introduce a family (line) of software products. We tune and evaluate our proposed methods toward mass emergencies. A mobile device takes on a heightened role in emergency situations, as they may suddenly be among their owner's only resources. This is a unique opportunity for software engineers to equip mobile devices with a software solution (also known as a mobile app). During my PhD and only by focusing on textual communication in Twitter during the Fort McMurray wildfire, I showed that 85% of the top 40 essential features were not available in any of the existing emergency apps. The research results covered in national and international media triggered numerous collaboration requests. In this proposal we follow three main objectives: - Developing methods to infer requirements from textual and non-textual general-purpose communications using machine learning. - Developing an automated method for defining app features for satisfying requirements using data statistics and mining methods. - Offering a mobile app product line for mass emergencies with an optimized set of features from using hyper and metaheuristic search-based techniques. This cross-disciplinary research proposal has tangible impact on the daily life of people. I plan to fund 2 PhD, 2 Masters, and 5 undergraduate or high-school students with this research proposal on problems relevant to Canadian society.