The Last Mile: Adapting Software Analytics into Developers' Context

软件分析研究领域极大地改进了我们可能称之为软件开发的“科学”，例如理解工件空间中的潜在关系以及不同预测方法的可能准确性。然而，这一研究领域也因缺乏实际结果、工具和技术而受到批评，这些结果、工具和技术可能被开发人员和管理人员视为“可操作的”。例如，缺陷预测的主题----，其中基于历史和/或程序结构的统计模型被用来预测哪些文件最有可能包含错误----可能在该领域拥有最强的研究成果。虽然通常陈述的缺陷预测的动机似乎是合理的----在实践中，额外的资源可能用于标记为容易出现缺陷的源文件----，但是行业采用这些方法的速度很慢，因为所获得的知识几乎没有实际价值。此外，虽然大量的研究工作已经花费在使用重量级分析技术（如机器学习和自然语言处理）探索工件之间的各种复杂关系上，但工业软件分析的真正成功案例----（如在GitHub和主要开源项目的网页----上显而易见）一直是基于直接的指标创建简单的仪表板，例如跟踪开发人员在维护任务上的活动。从根本上说，软件分析研究人员花时间探索的内容与工业开发人员需要的信息之间存在脱节；这种脱节在软件分析研究社区中是众所周知的。这个研究项目的目标是双重的：改进建模和对开发人员和管理人员的信息需求的理解，并且，举例来说，建立从这些模型到软件分析研究社区的技术之间的桥梁。这样做，我们将有助于跨越研究实践和工业需求之间的“最后一英里”。这项工作将有助于加拿大软件行业在其工作流程中变得更加有效；开发人员将能够更好地利用他们的工作环境来解决他们的日常问题，因为这些工具将更精细地调整到他们的实际信息需求。反过来，这将导致开发人员做出更明智的决策，更少的时间浪费，以及总体上更高质量的软件系统。

The software analytics research area has greatly improved what we might call the "science" of software development, such as understanding latent relationships in the artifact space and the likely accuracy of different prediction methods. However, this research area has also been criticized for a lack of practical results, tools, and techniques that might be deemed "actionable'' by developers and managers. For example, the topic of defect prediction ---- where statistical models based on history and/or program structure are employed to predict which files are the likeliest to contain bugs ---- has perhaps the strongest body of research results in this field. While the usual stated motivation for defect prediction might seem sound ---- extra resources may be devoted to source files marked as defect prone ---- in practice, industry has been slow to adopt these approaches, seeing little practical value to the knowledge gained. Furthermore, while much research effort has been spent exploring various complex relationships among artifacts using heavyweight analysis techniques such as machine learning and natural language processing, the real success stories of industrial software analytics ---- such as are evident on GitHub and the web pages of major open source projects ---- have been in creating simple dashboards based on straightforward metrics, such as tracking developer activities on maintenance tasks. Fundamentally, there is a disconnect between what software analytics researchers spend their time exploring and what information needs industrial developers have; this disconnect is well known within the software analytics research community. The goal of this research program is twofold: to improve modelling and understanding of the information needs of developers and managers, and, by example, to build bridges from these models to techniques from the software analytics research community. In so doing, we will help to span the "last mile" between research practice and industrial needs. This work will aid the Canadian software industry to become more effective in their work processes; developers will be better able to exploit their working context in solving their day-to-day problems, since the tools will be more finely tuned to their actual information needs. In turn, this will result in better informed decision-making by developers, less time wasted, and higher quality software systems overall.