An industrial engineering approach to software development
Journal of Systems and Software
Statistical Process Control for Software?
IEEE Software
Application of statistical process control to the software process
WADAS '92 Proceedings of the ninth Washington Ada symposium on Ada: Empowering software users and developers
Test process improvement: a practical step-by-step guide to structured testing
Test process improvement: a practical step-by-step guide to structured testing
Measuring the software process: statistical process control for software process improvement
Measuring the software process: statistical process control for software process improvement
Economics of software verification
PASTE '01 Proceedings of the 2001 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Practical Applications of Statistical Process Control
IEEE Software
Optimum Control Limits for Employing Statistical Process Control in Software Process
IEEE Transactions on Software Engineering
Agile Software Testing in a Large-Scale Project
IEEE Software
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Using sensitivity analysis to create simplified economic models for regression testing
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
Assessment of Software Process and Metrics to Support Quantitative Understanding
Software Process and Product Measurement
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Analysis of Problems in Testing Practices
APSEC '09 Proceedings of the 2009 16th Asia-Pacific Software Engineering Conference
A study on agility and testing processes in software organizations
Proceedings of the 19th international symposium on Software testing and analysis
Defining a catalog of indicators to support process performance analysis
Journal of Software Maintenance and Evolution: Research and Practice
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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Variation is inherent to a process, and process management demands understanding the nature of variation in quantitative terms, for evaluation and prediction purposes. This understanding requires the identification of process indicators that build the system of variation. To utilize quantitative techniques to understand and improve a software process, more indicators are needed than in a manufacturing process. The need to identify the indicators of a software process and the lack of a generic approach to assess the ability of a software process for quantitative management encouraged us to carry out a sequence of studies that resulted in the development of an Assessment Approach for Quantitative Process Management (A2QPM). This paper explains an application of the A2QPM to the test development process of an avionics software project and presents the results. The study aimed at understanding the effect of the test design stage and the effect of internal reviews as verification activities in test development, with respect to process productivity and product quality measures. The measurement data collected during the execution of the processes were analyzed by control charts to observe the evidence of process stability. The mean values of measurement data were utilized to make performance comparisons between the various executions of the test development process. The results showed that process productivity was unaffected, but the test procedure quality was positively influenced by the application of test design and internal reviews. The utilization of the A2QPM as a guide for the quantitative implementation enabled the systematic evaluation of the test development process and measures prior to analysis. This resulted in the identification of process clusters having stable variation.