BSR: a statistic-based approach for establishing and refining software process performance baseline
Proceedings of the 28th international conference on Software engineering
Managing change and reliability of distributed software system
International Journal of Information Systems and Change Management
Statistically Based Process Monitoring: Lessons from the Trench
ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
Optimized software process for fault handling in global software development
ICSP'08 Proceedings of the Software process, 2008 international conference on Making globally distributed software development a success story
What can software engineers learn from manufacturing to improve software process and product?
Journal of Intelligent Manufacturing
Defining a catalog of indicators to support process performance analysis
Journal of Software Maintenance and Evolution: Research and Practice
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Statistical control charts are the most commonly used tools to analyze and monitor process variation and stability. Control charts help us isolate nonrandom causes of variation by plotting a measured attribute of the process over time; the upper and lower control limits are empirically derived from the measurements of process variation over time. If a data point falls outside the control limits, we assume that a nonrandom cause of variation is present. It is important that the control limits appropriately reflect the expected behavior of the process being measured. Measuring the number of escaped defects will alert us to problems in the inspection process even though the control charts might not be showing anything abnormal.