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Teaching software professionals how to combine assessments (qualitative information) and benchmarking (quantitative information) this text aims to encourage better software analysis.
Teaching software professionals how to combine assessments (qualitative information) and benchmarking (quantitative information) this text aims to encourage better software analysis.
Practical approach to software measurement Contains hands-on industry experiences
""This is the single best book on software quality engineering and metrics that I've encountered."" --Capers Jones, from the Foreword"Metrics and Models in Software Quality Engineering, Second Edition," is the definitive book on this essential topic of software development. Comprehensive in scope with extensive industry examples, it shows how to measure software quality and use measurements to improve the software development process. Four major categories of quality metrics and models are addressed: quality management, software reliability and projection, complexity, and customer view. In addition, the book discusses the fundamentals of measurement theory, specific quality metrics and tools, and methods for applying metrics to the software development process.New chapters bring coverage of critical topics, including: In-process metrics for software testingMetrics for object-oriented software developmentAvailability metricsMethods for conducting in-process quality assessments and software project assessmentsDos and Don'ts of Software Process Improvement, by Patrick O'TooleUsing Function Point Metrics to Measure Software Process Improvement, by Capers Jones In addition to the excellent balance of theory, techniques, and examples, this book is highly instructive and practical, covering one of the most important topics in software development--quality engineering. 0201729156B08282002
Project Requirements: A Guide to Best Practices gives project managers tools they can assimilate and apply easily to improve project success rates, reduce development costs, reduce rework, and accelerate time to market. Based on experience and best practices, this valuable reference will help you: • Clarify real requirements before you initiate project work • Improve management of project requirements • Save time and effort • Manage to your schedule • Improve the quality of deliverables • Increase customer satisfaction and drive repeat business Project Requirements: A Guide to Best Practices provides project managers with a direct, practical strategy to overcome requirements challenges and manage requirements successfully.
Software is one of the most important products in human history and is widely used by all industries and all countries. It is also one of the most expensive and labor-intensive products in human history. Software also has very poor quality that has caused many major disasters and wasted many millions of dollars. Software is also the target of frequent and increasingly serious cyber-attacks. Among the reasons for these software problems is a chronic lack of reliable quantified data. This reference provides quantified data from many countries and many industries based on about 26,000 projects developed using a variety of methodologies and team experience levels. The data has been gathered between 1970 and 2017, so interesting historical trends are available. Since current average software productivity and quality results are suboptimal, this book focuses on "best in class" results and shows not only quantified quality and productivity data from best-in-class organizations, but also the technology stacks used to achieve best-in-class results. The overall goal of this book is to encourage the adoption of best-in-class software metrics and best-in-class technology stacks. It does so by providing current data on average software schedules, effort, costs, and quality for several industries and countries. Because productivity and quality vary by technology and size, the book presents quantitative results for applications between 100 function points and 100,000 function points. It shows quality results using defect potential and DRE metrics because the number one cost driver for software is finding and fixing bugs. The book presents data on cost of quality for software projects and discusses technical debt, but that metric is not standardized. Finally, the book includes some data on three years of software maintenance and enhancements as well as some data on total cost of ownership.
Poor quality continues to bedevil large-scale development projects, but few software leaders and practitioners know how to measure quality, select quality best practices, or cost-justify their usage. In The Economics of Software Quality, leading software quality experts Capers Jones and Jitendra Subramanyam show how to systematically measure the economic impact of quality and how to use this information to deliver far more business value. Using empirical data from hundreds of software organizations, Jones and Subramanyam show how integrated inspection, static analysis, and testing can achieve defect removal rates exceeding 95 percent. They offer innovative guidance for predicting and measuring defects and quality; choosing defect prevention, pre-test defect removal, and testing methods; and optimizing post-release defect reporting and repair. This book will help you Prove that improved software quality translates into strongly positive ROI and greatly reduced TCO Drive better results from current investments in debugging and prevention Use quality techniques to stay on schedule and on budget Avoid "hazardous" metrics that lead to poor decisions Important note: The audio and video content included with this enhanced eBook can be viewed only using iBooks on an iPad, iPhone, or iPod touch.
Going where no book on software measurement and metrics has previously gone, this critique thoroughly examines a number of bad measurement practices, hazardous metrics, and huge gaps and omissions in the software literature that neglect important topics in measurement. The book covers the major gaps and omissions that need to be filled if data about software development is to be useful for comparisons or estimating future projects. Among the more serious gaps are leaks in reporting about software development efforts that, if not corrected, can distort data and make benchmarks almost useless and possibly even harmful. One of the most common leaks is that of unpaid overtime. Software is a very labor-intensive occupation, and many practitioners work very long hours. However, few companies actually record unpaid overtime. This means that software effort is underreported by around 15%, which is too large a value to ignore. Other sources of leaks include the work of part-time specialists who come and go as needed. There are dozens of these specialists, and their combined effort can top 45% of total software effort on large projects. The book helps software project managers and developers uncover errors in measurements so they can develop meaningful benchmarks to estimate software development efforts. It examines variations in a number of areas that include: Programming languages Development methodology Software reuse Functional and nonfunctional requirements Industry type Team size and experience Filled with tables and charts, this book is a starting point for making measurements that reflect current software development practices and realities to arrive at meaningful benchmarks to guide successful software projects.
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions. - Presents best practices, hints, and tips to analyze data and apply tools in data science projects - Presents research methods and case studies that have emerged over the past few years to further understanding of software data - Shares stories from the trenches of successful data science initiatives in industry
"Papers presented at the Eighth International Conference on New Trends in Software Methodologies, Tools and Techniques, (SoMeT 09) held in Prague, Czech Republic ... from September 23rd to 25th 2009."--P. v.