Author(s)

Charles W. Butler

Department of Computer Information Systems, Colorado State University, Fort Collins, USA.

This paper applies software analytics to open source code. Open-source software gives both individuals and businesses the flexibility to work with different parts of available code to modify it or incorporate it into their own project. The open source software market is growing. Major companies such as AWS, Facebook, Google, IBM, Microsoft, Netflix, SAP, Cisco, Intel, and Tesla have joined the open source software community. In this study, a sample of 40 open source applications was selected. Traditional McCabe software metrics including cyclomatic and essential complexities were examined. An analytical comparison of this set of metrics and derived metrics for high risk software was utilized as a basis for addressing risk management in the adoption and integration decisions of open source software. From this comparison, refinements were added, and contemporary concepts of design and data metrics derived from cyclomatic complexity were integrated into a classification scheme for software quality. It was found that 84% of the sample open source applications were classified as moderate low risk or low risk indicating that open source software exhibits low risk characteristics. The 40 open source applications were the base data for the model resulting in a technique which is applicable to any open source code regardless of functionality, language, or size.

Cyclomatic Complexity, Essential Complexity, Module Design Complexity, Design Complexity, Integration Complexity, Local Data Complexity, Public Global Data Complexity, Parameter Data Complexity, Risk, Application Risk Score, Application Risk Classification

Butler, C. (2022) Software Analytics of Open Source Business Software. Journal of Software Engineering and Applications, 15, 150-164. doi: 10.4236/jsea.2022.155008.