Information System for Software Development Process Analysis Using DORA and SPACE Metrics

Abstract

we developed an information system for automated diagnostics of software development processes based on version-control data. We analyzed limitations of existing engineering analytics tools, which often focus on managerial monitoring of individual performance and may create incentives to optimize measured indicators rather than underlying processes, consistent with Goodhart’s law. We designed an architecture in which privacy protection and resistance to such metric-driven behavior are treated as requirements of the application programming interface rather than as user-interface features. Role-based access to data is enforced at the REST API level according to the defense-in-depth principle, with unauthorized requests rejected at the service layer rather than hidden only in the user interface. The methodological framework combines the five-metric DORA model with the SPACE framework for multidimensional assessment. We defined algorithms for computing lead time for changes, deployment frequency, change failure rate, decomposed merge-request cycle time, merge-request size distribution, and the Bus Factor for code modules from incrementally collected GitLab data. We implemented a minimum viable product with three role-based dashboards, paired visualization of speed and quality metrics, transparent calculation algorithms, report export, and Docker Compose deployment suitable for data-localization requirements. The system can be used to diagnose software development processes in teams working with GitLab.