Trackware: A Unified Socio-Technical Analytics Platform

This study introduced Trackware, a socio‑technical analytics platform designed to address persistent information asymmetry between engineering teams and organizational stakeholders. Trackware provides a unified platform, reducing manual reporting effort, by integrating an EVM translation layer and AI‑driven workload modeling that supports predictive project management. The primary technical contribution is the integration of concurrent data pipelines that convert raw VCS events into business‑oriented metrics within a four‑second response time. This is complemented by a five‑axis health model, presented via interactive visualizations, understandable by both technical and non‑technical users. The dual‑mode architecture supports GitHub‑based tracking as well as manual project‑management inputs, which ensures versatile applicability across open‑source and proprietary environments, maintaining excellent analytical depth. Experimental evaluation verified the accuracy of EVM calculations, the reliability of threshold‑based alerting, and the latency improvements achieved through concurrent API design. Identified restrictions, including API rate limitations, capped sampling, manual EV‑ratio estimation, and uniform health weighting, define a clear trajectory for future work. Planned enhancements such as GraphQL‑based acquisition, automated EVM inputs, weighted health scoring, commit‑timestamp burnout detection, and contextual benchmarking will make Trackware scalable and a fully automated socio‑technical analytics solution. Overall, Trackware highlights how combining established project‑management theory (EVM), modern version‑control analytics, and generative AI can yield practical tools that bridge the gap between the domains of engineering and leadership, ensuring aligned operation for both the groups with data‑driven visibility.

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