Technatomy employs probabilistic modeling in a CMMI Level 4-assessed environment for Agile Development to dramatically improve the planning, performance and delivery of software development projects.
Our patent-pending management tools, methods and models apply statistical measurement and predictive outcome estimates to software development processes that are inherently dynamic. Typically, in Agile development, requirements evolve through the collaborative effort of self-organizing, cross-functional teams of analysts, developers, testers and configuration managers. The Agile approach advocates adaptive planning, evolutionary development, early and continuous delivery, and flexibly responding to change. Our consistently reliable performance is predicated on real-time, predictive estimation and management capabilities including an Agile Development Process Performance Baseline (PPB), Agile Development Estimation Tool (ADET) Process Performance Models (PPM) and an Agile Project Performance (APP) tool. Collectively this suite of tools, along with their proprietary algorithms, deliver quantitative planning information and measurable, probabilistic forecasting of development performance with high confidence levels. The performance data used to train the models is based on five years of software application projects spanning new development, sustainment, maintenance and DevOps.
Past performance project data is used to train algorithms that develop the average (AVG), upper control limit (UCL), and lower control limit (LCL) project performance boundaries used in measuring project, development, testing and documentation velocity rates. The PPB results inform the ADET PPM, which performs Monte Carlo Simulation (MCS) for the current project. The ADET PPM produces a probability distribution for program metrics of inherent uncertainty—requirements, defects created, defect points, total sprints and velocity variance. The ADET PPM planning results are recorded in our proprietary APP Tool, included in an overarching Project Workbook, along with actual project data measurements (e.g., actual sprint velocity) taken during project execution. The Project Manager analyzes project data against ADET PPB norms, and as needed, uses the ADET PPM to perform what-if analysis and recalculate project estimates.
Collectively, these tools, techniques and models bring an industry-first level of planning, insight and execution to agile software development and provide government sponsors with predictive insights into software development performance.