Optimising Software Lifecycle Management through Predictive Maintenance: Insights and Best Practices
Abhishek Goyal
Abstract
Predictive maintenance within SLM continues to grow as the revolutionary approach that optimises availability and enhances durability and performance of software systems while minimising the extent of downtime. This paper looks at the incorporation of predictive analysis in the SDLC mainly to forecast when software programs are likely to fail in an effort to minimise downtime. Using advanced technologies like machine learning, time series analysis, log mining, and automated testing, organisations can begin looking at ways to improve the ability to head off problems and improve the quality of software while decreasing maintenance costs. The paper focuses on correcting, adaptive, perfective, and preventive maintenance and explains the role of predictive maintenance in anticipating and preventing developing flaws. In addition, the advantages of adopting predictive maintenance in the software lifecycle are explained, which include safety, longer life span of assets, and a better fit with the keywords of Industry 4.0. The paper concludes with best practices for successfully incorporating predictive maintenance into SLM, emphasising data-driven decision-making, aligning maintenance strategies with business objectives, and ensuring continuous system optimisation.