Improving socio-technical coordination in development teams to avoid software failures

软件开发本质上是一项合作努力。大型开发团队需要有效地协调他们的工作，以便成功地管理代码中的频繁更改，整合他们的工作并避免失败。然而，协调来自大型团队的贡献是非常具有挑战性的，这些团队通常由数百人组成，他们可能跨越多个办公室、城市，甚至是大陆。这项研究的目标是通过在开发人员的开发环境中主动确定和呈现有关合作者的信息来改善开发人员协作，从而避免因代码更改而导致的失败。在我们的方法中，我们对开发人员进行实地研究，并开发社会技术协调的模型和相关措施，以及使用项目历史数据来确定项目中相关合作者的技术。这项研究是与我们的行业合作伙伴IBM RTC开发团队(也称为Jazz团队)合作进行的。我们分析可以追溯到代码更改的软件故障，以及参与这些更改的开发人员的协调模式。基于本项目开发的社会技术协调措施，我们开发了识别与故障有重要关系的协调模式的技术。我们希望将我们的技术实现到协作推荐器原型中，该原型可以自动识别那些参与到与导致失败的更改显著相关的协调模式中的开发人员。我们将通过与IBM RTC团队的用户研究来评估我们的技术并开发原型。这项研究将为研究和实践带来贡献。对于研究人员，我们将开发一种描述和衡量与软件故障相关的社会技术协调的方法，以及可用于自动识别对避免软件故障至关重要的合作者的技术。通过将我们的原型合并到Jazz项目中，Jazz团队可以在他们的日常协作活动中使用它，以增强其对软件团队分布式协作的现有支持，并最大限度地减少处理协调故障的时间。

Software development is inherently a collaborative endeavour. Large teams of developers need to effectively coordinate their efforts in order to successfully manage frequent changes in the code, to integrate their work and to avoid failures. However, coordinating the contributions from large teams, often of hundreds of people who may span multiple offices, cities, or even continents, is very challenging. The goal of this research is to improve developer collaboration by actively determining and presenting in the developers' development environment information about relevant collaborators so that failures caused by changes to code can be avoided. In our methodology we conduct field studies of developers, and develop a model of and associated measures of socio-technical coordination, as well as techniques that use project historical data to determine relevant collaborators in the project. This research is conducted in collaboration with our industrial partner, the IBM RTC development team (also known as the Jazz team). We analyze software failures that can be traced to changes in the code, and the coordination patterns of developers involved in those changes. Based on measures for socio-technical coordination developed in this project, we develop techniques for the identification of coordination patterns that significantly relate to failures. We expect to implement our techniques into a collaboration recommender prototype that automatically identifies those developers involved in coordination patterns that significantly relate to failure-inducing changes. We will evaluate our techniques and developed prototype through user studies with the IBM RTC team. This research will bring contributions to both research and practice. For researchers, we will develop a means of describing and measuring socio-technical coordination as related to software failures, as well as techniques that can be leveraged to automatically identify collaborators that are critical to avoid software failures. By incorporating our prototype into the Jazz project, the Jazz team can use it in their daily collaboration activities to enhance its existing support to distributed collaboration of software teams, and to minimize the time handling coordination breakdowns.