Managing Code Inspection Information
IEEE Software
Design and code inspections to reduce errors in program development
IBM Systems Journal
Measuring the software process: statistical process control for software process improvement
Measuring the software process: statistical process control for software process improvement
Practical Applications of Statistical Process Control
IEEE Software
Optimum Control Limits for Employing Statistical Process Control in Software Process
IEEE Transactions on Software Engineering
Continuous Software Process Improvement through Statistical Process Control
CSMR '05 Proceedings of the Ninth European Conference on Software Maintenance and Reengineering
Experiences of applying SPC techniques to software development processes
Proceedings of the 28th international conference on Software engineering
Improvement of causal analysis using multivariate statistical process control
Software Quality Control
Interpreting the CMMI: A Process Improvement Approach, Second Edition
Interpreting the CMMI: A Process Improvement Approach, Second Edition
SPW/ProSim'06 Proceedings of the 2006 international conference on Software Process Simulation and Modeling
Investigating suitability of software process and metrics for statistical process control
EuroSPI'06 Proceedings of the 13th European conference on Software Process Improvement
Systematic review of statistical process control: an experience report
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
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Techniques for statistical process control (SPC), such as using a control chart, have recently garnered considerable attention in the software industry. These techniques are applied to manage a project quantitatively and meet established quality and process-performance objectives. Although many studies have demonstrated the benefits of using a control chart to monitor software development processes (SDPs), some controversy exists regarding the suitability of employing conventional control charts to monitor SDPs. One major problem is that conventional control charts require a large amount of data from a homogeneous source of variation when constructing valid control limits. However, a large dataset is typically unavailable for SDPs. Aggregating data from projects with similar attributes to acquire the required number of observations may lead to wide control limits due to mixed multiple common causes when applying a conventional control chart. To overcome these problems, this study utilizes a Q chart for short-run manufacturing processes as an alternative technique for monitoring SDPs. The Q chart, which has early detection capability, real-time charting, and fixed control limits, allows software practitioners to monitor process performance using a small amount of data in early SDP stages. To assess the performance of the Q chart for monitoring SDPs, three examples are utilized to demonstrate Q chart effectiveness. Some recommendations for practical use of Q charts for SDPs are provided.