Forecasting And Simulating Software Development Projects

Download or read book Forecasting and Simulating Software Development Projects written by Troy Magennis and published by . This book was released on 2011-10-25 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting and Simulating Software Development Projects explains how to effectively model Kanban and Scrum projects to get accurate forecasts of cost, delivery dates and staff requirements. Modeling using Monte-carlo simulation allows rapid what-if analysis to find options that minimize cost and delivery time, whilst maximizing revenue. Simulation lets you hit target delivery dates, and shows the impact of hiring (or losing) staff with certain skillsets, taking software project leadership to a new level of maturity. Target audience and key takeaways - Project Managers: Understand modeling and forecast projects, and how to simulate those models to answer questions regarding delivery dates, cost, and staffing needs. Development Managers and Team Leads: Understand how to reduce the amount of estimation required for cost and date forecasts, and determining what development events cause the most impact. Executive Leadership: Understand how multiple teams can co-ordinate their forecasts in a methodical way, and provide a consistent approach to risk management and decision making. Venture Capital Investors: Understand how to obtain reliable cost and date forecasts for potential investments and how to compare different software project investment portfolios. Topics include - Simulating Scrum and Kanban project methodologies Forecasting the probability of hitting delivery date & costs Hiring the right team size and skill mix Creating visual animations and videos to sell solutions to others Finding what model inputs are critical to delivery date Effective (and minimal) story estimation and grouping strategies Capturing the project deliverables and story backlog Modeling development events: defects, added scope and blocking events Reverse engineering real-world data to improve model accuracy