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以称之为软件开发的“科学”，例如理解构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认为是“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-可能具有该领域中最强大的研究结果。虽然通常声明的缺陷预测动机可能看起来很合理-额外的资源可能会被投入到标记为容易出现缺陷的源文件上-在实践中，行业采用这些方法的速度很慢，认为所获得的知识几乎没有实际价值。此外，虽然使用机器学习和自然语言处理等重量级分析技术探索构件之间的各种复杂关系已经花费了大量的研究工作，但工业软件分析的真正成功故事-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，在软件分析研究人员花费他们的时间探索什么和工业开发人员需要什么信息之间存在着脱节；这种脱节在软件分析研究界是众所周知的。该研究计划的目标有两个：改进开发人员和管理人员的信息需求的建模和理解，并通过举例，建立从这些模型到软件分析研究社区的技术的桥梁。通过这样做，我们将帮助跨越研究实践和工业需求之间的“最后一英里”。这项工作将帮助加拿大软件行业提高工作流程的效率；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，因为工具将更好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，减少浪费的时间，并从整体上提高软件系统的质量。

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.