Implementing Effective Changes in Software Projects to Optimize Runtimes and Minimize Defects
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913796869586944 |
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| author | Gupta, Kartik |
| author_facet | Gupta, Kartik |
| contents | The continuous evolution of software projects necessitates the implementation of changes to enhance performance and reduce defects. This research explores effective strategies for learning and implementing useful changes in software projects, focusing on optimizing runtimes and minimizing software defects. A comprehensive review of existing literature sets the foundation for understanding the current landscape of software optimization and defect reduction. The study employs a mixed-methods approach, incorporating both qualitative and quantitative data from software projects before and after changes were made. Key methodologies include detailed data collection on runtimes and defect rates, root cause analysis of common issues, and the application of best practices from successful case studies. The research highlights critical techniques for learning from past projects, identifying actionable changes, and ensuring their effective implementation. In-depth case study analysis provides insights into the practical challenges and success factors associated with these changes. Statistical analysis of the results demonstrates significant improvements in runtimes and defect rates, underscoring the value of a structured approach to software project optimization. The findings offer actionable recommendations for software development teams aiming to enhance project performance and reliability. This study contributes to the broader understanding of software engineering practices, providing a framework for continuous improvement in software projects. Future research directions are suggested to refine these strategies further and explore their application in diverse software development environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Implementing Effective Changes in Software Projects to Optimize Runtimes and Minimize Defects Gupta, Kartik Software Engineering The continuous evolution of software projects necessitates the implementation of changes to enhance performance and reduce defects. This research explores effective strategies for learning and implementing useful changes in software projects, focusing on optimizing runtimes and minimizing software defects. A comprehensive review of existing literature sets the foundation for understanding the current landscape of software optimization and defect reduction. The study employs a mixed-methods approach, incorporating both qualitative and quantitative data from software projects before and after changes were made. Key methodologies include detailed data collection on runtimes and defect rates, root cause analysis of common issues, and the application of best practices from successful case studies. The research highlights critical techniques for learning from past projects, identifying actionable changes, and ensuring their effective implementation. In-depth case study analysis provides insights into the practical challenges and success factors associated with these changes. Statistical analysis of the results demonstrates significant improvements in runtimes and defect rates, underscoring the value of a structured approach to software project optimization. The findings offer actionable recommendations for software development teams aiming to enhance project performance and reliability. This study contributes to the broader understanding of software engineering practices, providing a framework for continuous improvement in software projects. Future research directions are suggested to refine these strategies further and explore their application in diverse software development environments. |
| title | Implementing Effective Changes in Software Projects to Optimize Runtimes and Minimize Defects |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2504.12300 |