Designing a ARRHHO with MCFSA-CNN based Models for DevOps practices in Software Organizations

Best practises for deploying DevOps have been identified through study utilising the freely accessible HELENA2 dataset. Additionally, an adaptive relative reflection Harris Hawks optimisation (ARHHO) is utilised as a feature selection, which broadens the scope of traditional HHO, solves the issue of local optimal solution stagnation, by a mapping-based cuttlefish optimisation method (MCFA) to create a forecast model for DevOps deployment. Based on this research, the next four practices are the most vital for managing DevOps practises efficiently across the life cycle of projects. This study’s contribution goes beyond simply finding the most effective DevOps techniques; I t also allows for the forecasting of DevOps project success and the prioritisation of these techniques. Assists software companies in causal which best practises to prioritise depending on the specifics of their projects. After the MCFSA-CNN perfect attained the accuracy of 95.69 and the MCC rate as 92.01 then preccision rate as 96.24 then recall rate as 94.76 and F1-score as 95.48 correspondingly.