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4.4.2 Measurement‐Driven Systems Engineering Using Six Sigma Techniques to Improve Software Defect Detection
Author(s) -
Selby Paige C.,
Selby Richard W.
Publication year - 2007
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2007.tb02900.x
Subject(s) - six sigma , computer science , dmaic , root cause analysis , software , measure (data warehouse) , software development , control chart , reliability engineering , systems engineering , software engineering , engineering , data mining , operations management , lean manufacturing , operating system , process (computing)
System developers and managers continually strive to identify, undertake, and realize improvements in system development methods. Measurement‐driven systems engineering guides the identification of improvement opportunities and enables the quantitative evaluation of progress and benefits. Six Sigma techniques provide a structured approach for using measurement‐driven methods to decrease the variances and shift the means of user‐defined metrics such as defect densities, development cycletimes, and resource expenditures. This research investigates the effectiveness of software defection detection using peer reviews across 12 system development phases on 14 large‐scale systems. This study analyzes 3418 defects from 731 peer reviews and benchmarks the defect injection and detection performance across the 12 system development phases. Six Sigma techniques including the define‐measure‐analyze‐improve‐control (DMAIC) method, root cause analysis, and control charts helped achieve inphase detection of 95 percent of defects and realize over 50 percent improvements in defect densities and closure cycletimes for certain peer review types.