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 us...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.展开更多
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 ...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.展开更多
Researchers have often commented on the high correlation between McCabe’s Cyclomatic Complexity (CC) and lines of code (LOC). Many have believed this correlation high enough to justify adjusting CC by LOC or even sub...Researchers have often commented on the high correlation between McCabe’s Cyclomatic Complexity (CC) and lines of code (LOC). Many have believed this correlation high enough to justify adjusting CC by LOC or even substituting LOC for CC. However, from an empirical standpoint the relationship of CC to LOC is still an open one. We undertake the largest statistical study of this relationship to date. Employing modern regression techniques, we find the linearity of this relationship has been severely underestimated, so much so that CC can be said to have absolutely no explana-tory power of its own. This research presents evidence that LOC and CC have a stable practically perfect linear rela-tionship that holds across programmers, languages, code paradigms (procedural versus object-oriented), and software processes. Linear models are developed relating LOC and CC. These models are verified against over 1.2 million randomly selected source files from the SourceForge code repository. These files represent software projects from three target languages (C, C++, and Java) and a variety of programmer experience levels, software architectures, and de-velopment methodologies. The models developed are found to successfully predict roughly 90% of CC’s variance by LOC alone. This suggest not only that the linear relationship between LOC and CC is stable, but the aspects of code complexity that CC measures, such as the size of the test case space, grow linearly with source code size across lan-guages and programming paradigms.展开更多
The demand for quality software expands as the business environment grows internationally. Common to all software development methodologies is a design phase that focuses on the physical software model. Design criteri...The demand for quality software expands as the business environment grows internationally. Common to all software development methodologies is a design phase that focuses on the physical software model. Design criteria used to evaluate these models are important for refinement and improvement. The purpose of this research is to apply transformations of McCabe’s cyclomatic complexity as a means for improving software design and assessing risks within a design. In this research, two metrics, the management (<em>C<sub>MGT</sub></em>) and maintenance (<em>C<sub>MN</sub></em>) coefficients are introduced, and they are used to address the architectural, size, and quality dimensions of a software design.展开更多
文摘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.
文摘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.
文摘Researchers have often commented on the high correlation between McCabe’s Cyclomatic Complexity (CC) and lines of code (LOC). Many have believed this correlation high enough to justify adjusting CC by LOC or even substituting LOC for CC. However, from an empirical standpoint the relationship of CC to LOC is still an open one. We undertake the largest statistical study of this relationship to date. Employing modern regression techniques, we find the linearity of this relationship has been severely underestimated, so much so that CC can be said to have absolutely no explana-tory power of its own. This research presents evidence that LOC and CC have a stable practically perfect linear rela-tionship that holds across programmers, languages, code paradigms (procedural versus object-oriented), and software processes. Linear models are developed relating LOC and CC. These models are verified against over 1.2 million randomly selected source files from the SourceForge code repository. These files represent software projects from three target languages (C, C++, and Java) and a variety of programmer experience levels, software architectures, and de-velopment methodologies. The models developed are found to successfully predict roughly 90% of CC’s variance by LOC alone. This suggest not only that the linear relationship between LOC and CC is stable, but the aspects of code complexity that CC measures, such as the size of the test case space, grow linearly with source code size across lan-guages and programming paradigms.
文摘The demand for quality software expands as the business environment grows internationally. Common to all software development methodologies is a design phase that focuses on the physical software model. Design criteria used to evaluate these models are important for refinement and improvement. The purpose of this research is to apply transformations of McCabe’s cyclomatic complexity as a means for improving software design and assessing risks within a design. In this research, two metrics, the management (<em>C<sub>MGT</sub></em>) and maintenance (<em>C<sub>MN</sub></em>) coefficients are introduced, and they are used to address the architectural, size, and quality dimensions of a software design.
