Maintenance of large-scale software is difficult due to large size and high complexity of code.80% of software development is on maintenance and the other 60% is on trying to understand the code. The severity of the code smells must be measured as well as fairness on it because it will help the developers especially in large scale source code projects. Code smell is not a bug in the system as it doesn’t prevent the program from functioning but it may increase the risk of software failure or performance slowdown. Therefore, this paper seeks to help developers with early prediction of severity of code smells and test the level of fairness on the predictions especially in large scale source code projects. Data is the collection of facts and observations in terms of events, it is continuously growing, getting denser and more varied by the minute across different disciplines or fields. Hence, Big Data emerged and is evolving rapidly, the various types of data being processed are huge, but no one has ever thought of where this data resides, we therefore noticed this data resides in software’s and the codebases of the software’s are increasingly growing that is the size of the modules, functionalities, the size of the classes etc. Since data is growing so rapidly it also mean the codebases of software’s or code are also growing as well. Therefore, this paper seeks to discuss the 5V’s of big data in the context of software code and how to optimize or manage the big code. When we talk of "Big Code for Big Software's," we are referring to the specific challenges and considerations involved in developing, managing, and maintaining of code in large-scale software systems.

The continuous integration (CI) and continuous delivery/deployment (CD) methods are key tools in the field of modern software development, and they assist in the rapid, reliable and quality delivery of software. These DevOps methods are automated, and the code development, testing, and deployment processes are streamlined, which reduces the risk of integration, enhances productivity, and minimizes human labor. To implement CI/CD, Java cloud applications can utilize cloud-native services such as AWS Code Pipeline, Azure DevOps, and Google Cloud Build, as well as tools like Jenkins, GitLab CI/CD, GitHub Actions, CircleCI, Travis CI, and Bamboo. Basic concepts of CI/CD include automation, regular integration, testing, intensive testing, constant feedback, and process improvement. Some of the major pipeline phases include deployment, monitoring, testing, artefact management, build automation, and source code management. Despite clear benefits, challenges remain, including infrastructure complexity, dependency management, test reliability, and cultural barriers, particularly in large-scale or enterprise Java projects. This work provides a thorough analysis of CI/CD procedures and resources, including frameworks, best practices, and challenges for Java cloud applications. It highlights strategies to optimize adoption, improve software quality, and accelerate delivery cycles.