Advances in Computers: Quality Software DevelopmentMarvin Zelkowitz This volume of Advances in Computers is number 66 in the series that began back in 1960. This series presents the ever changing landscape in the continuing evolution of the development of the computer and the field of information processing. Each year three volumes are produced presenting approximately 20 chapters that describe the latest technology in the use of computers today. Volume 66, subtitled "Quality software development," is concerned about the current need to create quality software. It describes the current emphasis in techniques for creating such software and in methods to demonstrate that the software indeed meets the expectations of the designers and purchasers of that software. - In-depth surveys and tutorials on software development approaches - Well-known authors and researchers in the field - Extensive bibliographies with most chapters - All chapters focus on software development issues - Discussion of high end computing applications, a topic generally not understood by most software professionals |
Contents
Quality Problem in Software Measurement Data | 43 |
Requirements Management for Dependable Software Systems | 79 |
Mechanics of Managing Software Risk | 143 |
The PERFECT Approach to ExperienceBased Process Evolution | 173 |
The Opportunities Challenges and Risks of High Performance Computing in Computational Science and Engineering | 239 |
Author Index | 303 |
| 311 | |
Contents of Volumes in This Series | 323 |
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Advances in Computers: Quality Software Development Marvin Zelkowitz, Ph.D., MS, BS. No preview available - 2006 |
Common terms and phrases
activities agile methods algorithms analysis application artifacts B/CR behavior benefits capability Capability Maturity Model challenges classifiers CMMI code development code project code team company’s processes complex components computational science constraints cost cross-validation Data Mining dataset defects defined dependability documentation effects efficient Engrg Ensemble Filter Ensemble-Partitioning Filter environment estimate example experience filtering level goals identify IEEE Software impact implementation improvement game plan Inspections instances interfaces investment iterations Machine Learning Maturity Model measured ment metrics noise noisy operational PEDAL framework predictions problems process architecture process change agents process evolution exercises process performance process set processors product planning project management PSPsm schedule SLOC software development software development processes Software Engineering Software Engineering Institute Software Metrics software process improvement software project software quality specific SPI method stakeholders SW-CMM Table tion TSPsm users validation verified WebGuide
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Page x - ... Institute (ISI). He manages research in computer architecture, VLSI, compilers, and other software tools. Prior to joining ISI, he was the head of the High Performance Computing Research department in the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. He oversaw work in scientific data management, visualization, numerical algorithms, and scientific applications. Prior to joining NERSC, Dr. Lucas was the deputy director of DARPA's Information...
Page 22 - Rather than attempt to put a dollar tag on benefits that by their nature are difficult to quantify, managers should reverse the process and estimate first how large these benefits must be in order to justify the proposed investment.
Page xi - He is a fellow of the American Physical Society and the American Institute of Aeronautics and Astronautics.
Page x - Wolf is professor of electrical engineering at Princeton University. Before joining Princeton, he was with AT&T Bell Laboratories, Murray Hill, New Jersey. He received his BS, MS, and Ph.D. degrees in electrical engineering from Stanford University in 1980, 1981, and 1984, respectively. His research interests include embedded computing, VLSI systems, and multimedia information systems.
Page 21 - ... analysis. Zero is, after all, no less arbitrary than any other number. Conservative accountants who assign zero values to many intangible benefits prefer being precisely wrong to being vaguely right. Managers need not follow their example.