More Agile Testing Learning Journeys for the Whole Team

Agile.Estimating.and.Planning.pdf

[用户故事与敏捷方法].(User.Stories.Applied：For.Agile.Software.Development).Mike.Cohn.文字版.pdf

这是一个敏捷QA过程方面的指导，对Scrum项目有帮助

Agile Experience Design A Digital Designer’s Guide to Agile, Lean, and Continuous Lindsay Ratcliffe and Marc McNeill

Agile-PLM系统简介.pdf

Addison.Wesley.Practices.for.Scaling.Lean.and.Agile.Development.Jan.2010.rar

“When will it be done?” That is probably the first question your customers ask you once you start working on something for them. Think about how many times you have been asked that question. How many times have you ever actually been right? We can debate all we want whether this is a fair question to ask given the tremendous amount of uncertainty in knowledge work, but the truth of the matter is that our customers are going to inquire about completion time whether we like it or not. Which means we need to come up with an accurate way to answer them. The problem is that the forecasting tools that we currently utilize have made us ill-equipped to provide accurate answers to reasonable customer questions. Until now. Topics Include Why managing for flow is the best strategy for predictability—including an introduction to Little’s Law and its implications for flow. A definition of the basic metrics of flow and how to properly visualize those metrics in analytics like Cumulative Flow Diagrams and Scatterplots. Why your process policies are the potentially the biggest reason that you are unpredictable. Table of Contents PART ONE - FLOW FOR PREDICTABILITY Chapter 1 - Flow, Flow Metrics, and Predictability Chapter 2 - The Basic Metrics of Flow Chapter 3 - Introduction to Little’s Law PART TWO - CUMULATIVE FLOW DIAGRAMS FOR PREDICTABILITY Chapter 4 - Introduction to CFDs Chapter 5 - Flow Metrics and CFDs Chapter 6 - Interpreting CFDs Chapter 7 - Conservation of Flow Part I Chapter 8 - Conservation of Flow Part II Chapter 9 - Flow Debt PART THREE - CYCLE TIME SCATTERPLOTS FOR PREDICTABILITY Chapter 10 - Introduction to Cycle Time Scatterplots Chapter 10a - Cycle Time Histograms Chapter 11 - Interpreting Cycle Time Scatterplots Chapter 12 - Service Level Agreements PART FOUR - PUTTING IT ALL TOGETHER FOR PREDICTABILITY Chapter 13 - Pull Policies Chapter 14 - Introduction to Forecasting Chapter 15 - Monte Carlo Method Introduction Chapter 16 - Getting Started PART FIVE - A

Project tracking systems, test and build tools, source control, continuous integration, and other built-in parts of the software development lifecycle generate a wealth of data that can be used to track and improve the quality and performance of products, processes, and teams. Although the iterative nature of Agile development is perfect for data-driven continuous improvement, the collection, analysis, and application of meaningful metrics often fades in favor of subjective measures that offer less insight into the real challenges of making better software. Agile Metrics in Action: Measuring and enhancing the performance of Agile teams is a practical book that shows how to take the data already being generated to make teams, processes, and products better. It points out which metrics to use to objectively measure performance and what data really counts, along with where to find it, how to get it, and how to analyze it. The book also shows how all team members can publish their own metrics through dashboards and radiators, taking charge of communicating performance and individual accountability. Along the way, it offers practical data analysis techniques, including a few emerging Big Data practices. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Table of Contents Part 1 Measuring agile teams Chapter 1 Measuring agile performance Chapter 2 Observing a live project Part 2 Collecting and analyzing your team’s data Chapter 3 Trends and data from project-tracking systems Chapter 4 Trends and data from source control Chapter 5 Trends and data from CI and deployment servers Chapter 6 Data from your production systems Part 3 Applying metrics to your teams, processes, and software Chapter 7 Working with the data you’re collecting: the sum of the parts Chapter 8 Measuring the technical quality of your software Chapter 9 Publishing metrics Chapter 10 Measuring your team against the agile principles Appendix A D

敏捷系统工程，Douglass博士的大作，2016年的新书。Douglass presents a vision of systems engineering in which precise specification of requirements, structure and behavior fuse with larger concerns, such as safety, security, reliability and performance in an agile engineering context.