Energy Efficient Software Development: An Integrated Approach for Green Computing and Enhanced Software Performance

Abstract

With the increasing adoption of green computing for sustainability, energy-efficient computing has become essential, particularly in software development. This study focuses on two critical software metrics: Cyclomatic Complexity and Software Defect Density. The goal is to achieve energy-efficient software development as an integrated approach to green computing and improved software performance. Specifically, the study models energy-efficient software development in the context of green computing using cyclomatic complexity and software defect density, simulates these models, and analyzes their impact on energy-efficient software development. Experimental methodology is adopted for the study. A sample Python code was used to illustrate cyclomatic complexity, while the JIRA tool was utilized to analyze various software versions for defects density. Energy quantization metrics were also employed to assess these effects on CPU utilization and memory usage. The research presented a framework that incorporates these metrics into the software development process to enhance energy efficiency. By minimizing decision points and addressing defects early on, the study demonstrated how software can be optimized to reduce carbon footprints while maintaining high performance. The results indicated that lowering complexity and defects leads to shorter processing times and improved resource utilization, aligning with the objectives of green computing. This approach provides valuable insights for software engineers seeking to create sustainable software that balances performance with environmental responsibility.