A quantitative analysis of the Carnegie Mellon Software Engineering Institute's Capability Maturity Model: A latent variable model
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| Publicado en: | ProQuest Dissertations and Theses (2000) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 020 | |a 978-0-599-92945-6 | ||
| 035 | |a 304587318 | ||
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| 100 | 1 | |a Chang, Shaoming | |
| 245 | 1 | |a A quantitative analysis of the Carnegie Mellon Software Engineering Institute's Capability Maturity Model: A latent variable model | |
| 260 | |b ProQuest Dissertations & Theses |c 2000 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a The Capability Maturity Model developed by Carnegie Mellon University's Software Engineering Institute (SEI/CMM) was the most commonly used method for enhancing software development/maintenance processes in the 1990s. The focus of SEI/CMM's research is to build a common production function for each of the five maturity levels (I to V) by comparing the behavior of the production functions of each level and then assessing the advantages and disadvantages of each production function for advancing the organization's maturity level. This research chose the Latent Variable Model (LVM) to fit the production function because in the world of software engineering, models must frequently be compromised or modified to reflect the sampled data. By introducing latent variables we could reproduce the most typical practical production environment. The LVM we studied is composed of the following two group of variables: (1) tangible variables, that is, sampled data (Function point, Duration, Man month, Team size); (2) intangible variables, that is, latent variables (Management effort, Production process). We also study Dr. Frederick Brooks' Man month myth among the five maturity levels. To analyze the differences, we then used the COCOMO model with an additional Team size factor. Does the Team size overhead go away at higher maturity levels or does it tag along? One of the purposes of this study was to find an answer. To conclude the Production function, the Dynamic model was used. | |
| 653 | |a Computer science | ||
| 653 | |a Models | ||
| 653 | |a Studies | ||
| 653 | |a Software engineering | ||
| 653 | |a Production planning | ||
| 653 | |a Management | ||
| 653 | |a Production functions | ||
| 653 | |a Behavior | ||
| 653 | |a Random access memory | ||
| 653 | |a Computer peripherals | ||
| 653 | |a Productivity | ||
| 653 | |a Cost estimates | ||
| 653 | |a Questionnaires | ||
| 653 | |a Scheduling | ||
| 653 | |a Software quality | ||
| 653 | |a Corporate culture | ||
| 653 | |a Planning | ||
| 653 | |a Variables | ||
| 653 | |a Copyright | ||
| 653 | |a Correlation analysis | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Integrated software | ||
| 653 | |a Performance management | ||
| 653 | |a Work environment | ||
| 653 | |a Software development | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2000) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/304587318/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/304587318/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |