<p dir="ltr">The research focuses on integrating automation, specifically security and Infrastructure as Code (IaC), into the Software Development Life Cycle (SDLC). This integration aims to enhance the efficiency, quality, and security of software development processes. The study explores the benefits and challenges associated with implementing DevSecOps practices, which combine development, security, and operations into a unified process.</p><h3>Background and Motivation</h3><p dir="ltr">The rise of new technologies and increasing demand for high-quality software have made software development a crucial aspect of business operations. The SDLC is essential for ensuring that software meets user requirements and maintains high standards of quality and security. Security, in particular, has become a critical focus due to the growing threat of cyber-attacks and data breaches. By integrating security measures early in the development process, companies can better protect their software and data.</p><h3>Objectives</h3><p dir="ltr">The primary objectives of this research are:</p><ol><li><b>Examine the Benefits and Challenges</b>: To investigate the advantages and difficulties of integrating DevSecOps and IaC within the SDLC.</li><li><b>Analyze Impact on Security and Quality</b>: To assess how automation affects the security and quality of software developed through the SDLC.</li><li><b>Develop a Framework</b>: To create a comprehensive framework for integrating DevSecOps and IaC into the SDLC, thereby improving security and reducing time to market.</li></ol><h3>Methodology</h3><p dir="ltr">The research employs a mixed-methods approach, combining qualitative and quantitative methods:</p><ul><li><b>Qualitative</b>: A literature review of existing research on DevSecOps, IaC, and SDLC, providing a theoretical foundation and context.</li><li><b>Quantitative</b>: Building a CI/CD (Continuous Integration/Continuous Deployment) pipeline from scratch to collect empirical data. This pipeline serves as a case study to gather insights into how automation impacts software security and quality.</li></ul><h3>Tools and Technologies</h3><p dir="ltr">The study utilizes various tools, including:</p><ul><li><b>GitHub</b>: For version control and code repository management.</li><li><b>Jenkins</b>: To automate the CI/CD pipeline, including building, testing, and deploying applications.</li><li><b>SonarQube</b>: For static code analysis, detecting code quality issues, and security vulnerabilities.</li><li><b>Amazon Q</b>: An AI-driven tool used for code generation and security scanning.</li><li><b>OWASP Dependency-Check</b>: To identify vulnerabilities in project dependencies.</li><li><b>Prometheus and Grafana</b>: For monitoring and collecting metrics.</li><li><b>Terraform</b>: For defining and deploying infrastructure components as code.</li></ul><h3>Key Findings</h3><ul><li><b>Reduction in Defect Density</b>: Automation significantly reduced defect density, indicating fewer bugs and higher code quality.</li><li><b>Increase in Code Coverage</b>: More comprehensive testing, leading to improved software reliability.</li><li><b>Reduction in MTTR, MTTD, and MTTF</b>: Enhanced system reliability and efficiency, with faster detection and resolution of issues.</li><li><b>Improved System Performance</b>: Better performance metrics, such as reduced response time and increased throughput.</li></ul><h3>Conclusion</h3><p dir="ltr">The study concludes that integrating security and IaC automation into the SDLC is crucial for improving software quality, security, and development efficiency. However, despite the clear benefits, many companies are hesitant to adopt these practices due to perceived challenges, such as the upfront investment, complexity of implementation, and concerns about ROI (Return on Investment). The research underscores the need for continued innovation and adaptation in software development practices to meet the evolving demands of the technological landscape.</p><h3>Areas for Further Research</h3><p dir="ltr">Future studies could explore the broader impact of automation on developer productivity, job satisfaction, and long-term security practices. There is also potential for developing advanced security analysis techniques using machine learning and artificial intelligence, as well as investigating the integration of security and compliance practices within automated SDLC frameworks.</p>