Software Metric Analysis of Open-Source Business Software  

作　　者：Charles W. Butler Charles W. Butler(Department of Computer Information Systems, Colorado State University, Fort Collins, USA)

机构地区：[1]Department of Computer Information Systems, Colorado State University, Fort Collins, USA

出　　处：《Journal of Software Engineering and Applications》2023年第5期144-153,共10页软件工程与应用（英文）

摘　　要：Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.

关 键 词：Open-Source Software Software Quality Software Risks Cyclomatic Complexity Essential Complexity Module Design Complexity Design Complexity Integration Complexity Local Data Complexity Public Global Data Complexity Parameter Data Complexity Risk Score Risk Classification 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]