More effective sprint retrospective with statistical analysis. Issue 5 (28th December 2017) - Erdoğan, Onur; Pekkaya, Muhammed Emre; Gök, Halime
Abstract: Scrum teams aim to deliver products productively with highest possible value and quality, so they try to deliver high priority and high value product backlog items in earlier sprints. Making size estimation of product backlog items correctly is one of the most prominent factors for effective sprint planning. Retrospective meetings are an opportunity for teams to improve product quality, their productivity, and estimation capability. Enhancing in those areas requires empiricism as agility requires; hence, measureable indicators should be inspected and adapted at regular intervals. In this study, we assessed how and what kind of historical data is required to be collected for monitoring, and how statistical analysis can be investigated for inspection and adaptation in retrospective meetings. We experimented that statistics of "Correlation between Story Point and Actual Effort" and "Consistency of Relative Estimation" were convenient for inspection and adaptation of estimation capability of teams. Past retrospective meetings also showed that statistics of "Team's Actual Effort on Product, " "Team Velocity, " "Actual Effort for One Story Point, " "Innovation Rate, " and "Velocity vs Unplanned Effort Rate" were helpful to control and increase the productivity of teams. "Actual Effort Rate of Quality Activities" and "Subcomponent Defect Density" statistical results helped to enhance product quality. Abstract : Scrum teams aim to deliver products productively with highest possible value and quality, so they try to deliver high‐priority and high‐value product backlog items in earlier sprints. Making size estimation of product backlog items correctly is one of the most prominent factors for effective sprint planning. In this study, we describe several metrics for quantitative scrum project management that can develop and sustain hyper‐productive teams in terms of team velocity, accurate relative estimation, work capacity, quality factors, unplanned work ratio, commitment accuracy, estimate accuracy, and innovation contribution increase.