Software Quality Assurance for Mathematical Modeling Systems

With increasing importance placed on standard quality assurance methodologies by large companies and government organizations, many software companies have implemented rigorous quality assurance (QA) processes to ensure that these standards are met. The satisfaction of these quality checks ensures that the software has achieved a certain level of compliance to standards and thereby gives the consumer confidence that the risk level in the software product is negligible or minimal. Furthermore, the use of QA methodologies cuts maintenance costs, increases reliability, and reduces cycle time for new distributions. Modeling systems differ from most software systems in that a model may fail to solve to optimality without the modeling system being defective. This additional level of complexity requires specific QA activities. To make software quality assurance (SQA) more cost-effective, the focus is on reproducible and automated techniques. In this paper we describe some of the main SQA methodologies as applied to modeling systems. In particular, we focus on configuration management, quality control, and testing as they are handled in the GAMS build framework, emphasizing reproducibility, automation, and an open-source public-domain framework. We give examples how quality assurance can be used for modeling systems in general to increase the quality of the product, reduce turnaround time for bug fixes and new software features, and reduce the time needed to create new distributions. (quality assurance; software; modeling systems; mathematical programming, automation)