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BSR: a statistic-based approach for establishing and refining software process performance baseline
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Proceedings of the 28th international conference on Software engineering table of contents
Shanghai, China
SESSION: Far east experience papers: software process table of contents
Pages: 585 - 594  
Year of Publication: 2006
ISBN:1-59593-375-1
Authors
Qing Wang  Chinese Academy of Sciences, Beijing, P.R. CHINA
Nan Jiang  Chinese Academy of Sciences, Beijing, P.R. CHINA and Graduate University of Chinese Academy of Sciences, Beijing, P.R. CHINA
Lang Gou  Chinese Academy of Sciences, Beijing, P.R. CHINA and Graduate University of Chinese Academy of Sciences, Beijing, P.R. CHINA
Xia Liu  Chinese Academy of Sciences, Beijing, P.R. CHINA and Graduate University of Chinese Academy of Sciences, Beijing, P.R. CHINA
Mingshu Li  Chinese Academy of Sciences, Beijing, P.R. CHINA
Yongji Wang  Chinese Academy of Sciences, Beijing, P.R. CHINA
Sponsors
ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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ABSTRACT

High-level process management is quantitative management. The Process Performance Baseline (PPB) of process or subprocess under statistical management is the most important concept. It is the basis of process control and improvement. The existing methods for establishing process baseline are too coarse-grained or have some limitation, which lead to inaccurate or ineffective quantitative management. In this paper, we propose an approach called BSR (Baseline-Statistic-Refinement) for establishing and refining software process performance baseline, and present the experience result to validate its effectiveness for quantitative process management.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Qing Wang: colleagues
Nan Jiang: colleagues
Lang Gou: colleagues
Xia Liu: colleagues
Mingshu Li: colleagues
Yongji Wang: colleagues