Although software engineering can trace its beginnings to a NATO conf- ence in 1968, it cannot be said to have become an empirical science until the 1970s with the advent of the work of Prof. Victor Robert Basili of the University of Maryland. In addition to the need to engineer software was the need to understand software. Much like other sciences, such as physics, chemistry, and biology, software engineering needed a discipline of obs- vation, theory formation, experimentation, and feedback. By applying the scientific method to the software engineering domain, Basili developed concepts like the Goal-Question-Metric method, the Quality-Improvement- Paradigm, and the Experience Factory to help bring a sense of order to the ad hoc developments so prevalent in the software engineering field. On the occasion of Basili's 65th birthday, we present this book c- taining reprints of 20 papers that defined much of his work. We divided the 20 papers into 6 sections, each describing a different facet of his work, and asked several individuals to write an introduction to each section. Instead of describing the scope of this book in this preface, we decided to let one of his papers, the keynote paper he gave at the International C- ference on Software Engineering in 1996 in Berlin, Germany to lead off this book. He, better than we, can best describe his views on what is - perimental software engineering.

The IT community has always struggled with questions concerning the value of an organization's investment in software and hardware. It is the goal of value-based software engineering (VBSE) to develop models and measures of value which are of use for managers, developers and users as they make tradeoff decisions between, for example, quality and cost or functionality and schedule - such decisions must be economically feasible and comprehensible to the stakeholders with differing value perspectives. VBSE has its roots in work on software engineering economics, pioneered by Barry Boehm in the early 1980s. However, the emergence of a wider scope that defines VBSE is more recent. VBSE extends the merely technical ISO software engineering definition with elements not only from economics, but also from cognitive science, finance, management science, behavioral sciences, and decision sciences, giving rise to a truly multi-disciplinary framework. Biffl and his co-editors invited leading researchers and structured their contributions into three parts, following an introduction into the area by Boehm himself. They first detail the foundations of VBSE, followed by a presentation of state-of-the-art methods and techniques. The third part demonstrates the benefits of VBSE through concrete examples and case studies. This book deviates from the more anecdotal style of many management-oriented software engineering books and so appeals particularly to all readers who are interested in solid foundations for high-level aspects of software engineering decision making, i.e., to product or project managers driven by economics and to software engineering researchers and students.

This book constitutes the thoroughly refereed post-proceedings of the International Software Process Workshop, SPW 2005, help in Beijing, China in May 2005. The 30 papers presented here, together with 11 keynote addresses are organized in topical sections on process content, process tools and metrics, process management, process representation and analysis, as well as experience reports.

The IT community has always struggled with questions concerning the value of an organizationa (TM)s investment in software and hardware. It is the goal of value-based software engineering (VBSE) to develop models and measures of value which are of use for managers, developers and users as they make tradeoff decisions between, for example, quality and cost or functionality and schedule a" such decisions must be economically feasible and comprehensible to the stakeholders with differing value perspectives. VBSE has its roots in work on software engineering economics, pioneered by Barry Boehm in the early 1980s. However, the emergence of a wider scope that defines VBSE is more recent. VBSE extends the merely technical ISO software engineering definition with elements not only from economics, but also from cognitive science, finance, management science, behavioural sciences, and decision sciences, giving rise to a truly multi-disciplinary framework.

Biffl and his co-editors invited leading researchers and structured their contributions into three parts, following an introduction into the area by Boehm himself. They first detail the foundations of VBSE, followed by a presentation of state-of-the-art methods and techniques. The third part demonstrates the benefits of VBSE through concrete examples and case studies.

This book deviates from the more anecdotal style of many management-oriented software engineering books and so appeals particularly to all readers who are interested in solid foundations for high-level aspects of software engineering decision making, i.e. to product or project managers driven by economics and to software engineering researchers and students.

Although software engineering can trace its beginnings to a NATO conf- ence in 1968, it cannot be said to have become an empirical science until the 1970s with the advent of the work of Prof. Victor Robert Basili of the University of Maryland. In addition to the need to engineer software was the need to understand software. Much like other sciences, such as physics, chemistry, and biology, software engineering needed a discipline of obs- vation, theory formation, experimentation, and feedback. By applying the scientific method to the software engineering domain, Basili developed concepts like the Goal-Question-Metric method, the Quality-Improvement- Paradigm, and the Experience Factory to help bring a sense of order to the ad hoc developments so prevalent in the software engineering field. On the occasion of Basili's 65th birthday, we present this book c- taining reprints of 20 papers that defined much of his work. We divided the 20 papers into 6 sections, each describing a different facet of his work, and asked several individuals to write an introduction to each section. Instead of describing the scope of this book in this preface, we decided to let one of his papers, the keynote paper he gave at the International C- ference on Software Engineering in 1996 in Berlin, Germany to lead off this book. He, better than we, can best describe his views on what is - perimental software engineering.

From the beginning of software time, people have wondered why it isn't possible to accelerate software projects by simply adding staff. This is sometimes known as the "nine women can't make a baby in one month" problem. The most famous treatise declaring this to be impossible is Fred Brooks' 1975 book The Mythical Man-Month, in which he declares that "adding more programmers to a late software project makes it later," and indeed this has proven largely true over the decades. Aided by a domain-driven code generator that quickly creates database and API code, Parallel Agile (PA) achieves significant schedule compression using parallelism: as many developers as necessary can independently and concurrently develop the scenarios from initial prototype through production code. Projects can scale by elastic staffing, rather than by stretching schedules for larger development efforts. Schedule compression with a large team of developers working in parallel is analogous to hardware acceleration of compute problems using parallel CPUs. PA has some similarities with and differences from other Agile approaches. Like most Agile methods, PA "gets to code early" and uses feedback from executable software to drive requirements and design. PA uses technical prototyping as a risk-mitigation strategy, to help sanity-check requirements for feasibility, and to evaluate different technical architectures and technologies. Unlike many Agile methods, PA does not support "design by refactoring," and it doesn't drive designs from unit tests. Instead, PA uses a minimalist UML-based design approach (Agile/ICONIX) that starts out with a domain model to facilitate communication across the development team, and partitions the system along use case boundaries, which enables parallel development. Parallel Agile is fully compatible with the Incremental Commitment Spiral Model (ICSM), which involves concurrent effort of a systems engineering team, a development team, and a test team working alongside the developers. The authors have been researching and refining the PA process for several years on multiple test projects that have involved over 200 developers. The book's example project details the design of one of these test projects, a crowdsourced traffic safety system.

From the beginning of software time, people have wondered why it isn't possible to accelerate software projects by simply adding staff. This is sometimes known as the "nine women can't make a baby in one month" problem. The most famous treatise declaring this to be impossible is Fred Brooks' 1975 book The Mythical Man-Month, in which he declares that "adding more programmers to a late software project makes it later," and indeed this has proven largely true over the decades. Aided by a domain-driven code generator that quickly creates database and API code, Parallel Agile (PA) achieves significant schedule compression using parallelism: as many developers as necessary can independently and concurrently develop the scenarios from initial prototype through production code. Projects can scale by elastic staffing, rather than by stretching schedules for larger development efforts. Schedule compression with a large team of developers working in parallel is analogous to hardware acceleration of compute problems using parallel CPUs. PA has some similarities with and differences from other Agile approaches. Like most Agile methods, PA "gets to code early" and uses feedback from executable software to drive requirements and design. PA uses technical prototyping as a risk-mitigation strategy, to help sanity-check requirements for feasibility, and to evaluate different technical architectures and technologies. Unlike many Agile methods, PA does not support "design by refactoring," and it doesn't drive designs from unit tests. Instead, PA uses a minimalist UML-based design approach (Agile/ICONIX) that starts out with a domain model to facilitate communication across the development team, and partitions the system along use case boundaries, which enables parallel development. Parallel Agile is fully compatible with the Incremental Commitment Spiral Model (ICSM), which involves concurrent effort of a systems engineering team, a development team, and a test team working alongside the developers. The authors have been researching and refining the PA process for several years on multiple test projects that have involved over 200 developers. The book's example project details the design of one of these test projects, a crowdsourced traffic safety system.