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所谓的软件开发的“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开发人员和管理人员可能认为“可行”的实际结果、工具和技术而受到批评。例如，缺陷预测的主题-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-可能是这个领域最强有力的研究成果。虽然通常陈述的缺陷预测的动机似乎是合理的-额外的资源可能被用于标记为缺陷倾向的源文件-在实践中，工业界采用这些方法的速度很慢，对所获得的知识几乎没有实际价值。此外，虽然已经花费了大量的研究工作，使用重量级的分析技术（如机器学习和自然语言处理）来探索工件之间的各种复杂关系，但工业软件分析的真实的成功案例--例如在GitHub和主要开源项目的网页上很明显--是基于简单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费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。该研究计划的目标是双重的：提高建模和开发人员和管理人员的信息需求的理解，并通过举例，建立从这些模型的桥梁，从软件分析研究社区的技术。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业提高工作效率;开发人员将能够更好地利用其工作环境解决日常问题，因为这些工具将更好地适应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，并提高软件系统的整体质量。

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.