The DevOps Research and Assessment (DORA) framework is the most widely adopted measurement system for performance measurement across engineering teams. However, every DORA metric is a first-moment statistic or a simple ratio, which limits the potential insights into engineering process. For example, metrics like Deployment Frequency do not capture the distributional shape of deployment timing, so teams with identical measures can deploy on a metronomic cadence or in undesirably erratic bursts. We have been developing and piloting Delivery Consistency (DC), a bounded second-moment measure of cadence regularity derived from the coefficient of variation of inter-release intervals. In conjunction with other DORA concepts, we integrated DC into the Delivery Health Matrix, an eight-archetype diagnostic that maps joint readings to differentiated interventions. We report an experience evaluation on a four-platform software delivery group using 120 weeks of data extracted from our Jira, GitHub, and Firebase records. DC allowed us to distinguish platforms with identical DORA tier placements but different cadence regularity, and the Matrix summarized the readings into an archetype that pointed at a shared organization or procedural constraint.

翻译：DevOps研究与评估（DORA）框架是工程团队间最广泛采用的性能度量体系。然而，每个DORA指标均为一阶统计量或简单比值，这限制了其揭示工程流程深层特征的潜力。例如，部署频率等指标无法捕捉部署时机的分布形态，导致具有相同测量值的团队可能以节拍式节奏稳定部署，也可能以不可控的突发方式频繁部署。我们开发并试点测试了交付一致性（DC）指标——一种基于发布间隔变异系数的有界二阶节律规律性度量。结合其他DORA概念，我们将DC集成至交付健康矩阵中，该八原型诊断工具可将联合读数映射至差异化干预措施。我们基于从Jira、GitHub和Firebase记录中提取的120周数据，对一个四平台软件交付组进行了经验评估。DC使我们能够区分具有相同DORA层级定位但节律规律性不同的平台，而交付健康矩阵将这些读数汇总为指向共享组织或流程约束的原型。