Few software projects are completed on time, on budget, and to their original specifications. Focusing on what practitioners need to know about risk in the pursuit of delivering software projects, Applied Software Risk Management: A Guide for Software Project Managers covers key components of the risk management process and the software development process, as well as best practices for software risk identification, risk planning, and risk analysis. Written in a clear and concise manner, this resource presents concepts and practical insight into managing risk. It first covers risk-driven project management, risk management processes, risk attributes, risk identification, and risk analysis. The book continues by examining responses to risk, the tracking and modeling of risks, intelligence gathering, and integrated risk management. It concludes with details on drafting and implementing procedures. A diary of a risk manager provides insight in implementing risk management processes. Bringing together concepts across software engineering with a project management perspective, Applied Software Risk Management: A Guide for Software Project Managers presents a rigorous, scientific method for identifying, analyzing, and resolving risk.

From the perspective that software measurements can be simple yet very useful in making the decisions needed to mange software projects, Software Measurement, Metrics and Project Management presents an integrated approach to measurements and techniques for deriving knowledge from measurements. The ideas and techniques are derived from best practices and are field-proven, down-to-earth, and above all, clearly stated. The author directly relates data to decision-making and leadership issues in business and provides outstanding guidance on data analysis interpretation and process modeling. Ultimately, it will help readers understand how ordinary analysis techniques can be applied to achieve extraordinary results.

Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. Simple Statistical Methods for Software Engineering: Data and Patterns fills that void. Instead of delving into overly complex statistics, the book details simpler solutions that are just as effective and connect with the intuition of problem solvers. Sharing valuable insights into software engineering problems and solutions, the book not only explains the required statistical methods, but also provides many examples, review questions, and case studies that provide the understanding required to apply those methods to real-world problems. After reading this book, practitioners will possess the confidence and understanding to solve day-to-day problems in quality, measurement, performance, and benchmarking. By following the examples and case studies, students will be better prepared able to achieve seamless transition from academic study to industry practices. Includes boxed stories, case studies, and illustrations that demonstrate the nuances behind proper application Supplies historical anecdotes and traces statistical methods to inventors and gurus Applies basic statistical laws in their simplest forms to resolve engineering problems Provides simple techniques for addressing the issues software engineers face The book starts off by reviewing the essential facts about data. Next, it supplies a detailed review and summary of metrics, including development, maintenance, test, and agile metrics. The third section covers the fundamental laws of probability and statistics and the final section presents special data patterns in the form of tailed mathematical distributions. In addition to selecting simpler and more flexible tools, the authors have also simplified several standard techniques to provide you with the set of intellectual tools al

Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. Simple Statistical Methods for Software Engineering: Data and Patterns fills that void. Instead of delving into overly complex statistics, the book details simpler solutions that are just as effective and connect with the intuition of problem solvers. Sharing valuable insights into software engineering problems and solutions, the book not only explains the required statistical methods, but also provides many examples, review questions, and case studies that provide the understanding required to apply those methods to real-world problems. After reading this book, practitioners will possess the confidence and understanding to solve day-to-day problems in quality, measurement, performance, and benchmarking. By following the examples and case studies, students will be better prepared able to achieve seamless transition from academic study to industry practices. Includes boxed stories, case studies, and illustrations that demonstrate the nuances behind proper application Supplies historical anecdotes and traces statistical methods to inventors and gurus Applies basic statistical laws in their simplest forms to resolve engineering problems Provides simple techniques for addressing the issues software engineers face The book starts off by reviewing the essential facts about data. Next, it supplies a detailed review and summary of metrics, including development, maintenance, test, and agile metrics. The third section covers the fundamental laws of probability and statistics and the final section presents special data patterns in the form of tailed mathematical distributions. In addition to selecting simpler and more flexible tools, the authors have also simplified several standard techniques to provide you with the set of intellectual tools al