Leading the way in innovation for over 55 years, we build greater futures for businesses across multiple industries and 55 countries.
Our expert, committed team put our shared beliefs into action – every day. Together, we combine innovation and collective knowledge to create the extraordinary.
We share news, insights, analysis and research – tailored to your unique interests – to help you deepen your knowledge and impact.
At TCS, we believe exceptional work begins with hiring, celebrating and nurturing the best people — from all walks of life.
Get access to a catalog of the latest news stories from across TCS. Discover our press releases, reports, and company announcements.
You have these already downloaded
We have sent you a copy of the report to your email again.
Enterprise operations are undergoing major transformations to cater to the latest customer requirements. In this evolving scenario, 75% enterprises are keen to migrate their applications to cloud as a part of their digital transformation journeys.
Typically, applications in large enterprises can be classified into:
1. Legacy applications that run on mainframe environments
2. Custom-built distributed applications that use .NET and Java
3. Web applications that use Java
4. Packaged applications that run on various platforms
The composition of these applications varies according to the industry to which the enterprise belongs, and the age and history of transformation initiatives executed till date in the enterprise. An application can also span across multiple categories. For example, a relatively large number of banking and financial enterprises have large batch-processing components in mainframe, while the front-end applications are built on web technology and application programming interfaces (APIs). Depending on the technologies involved, moving to cloud can either be an extremely simple no-touch, lift and shift process of cloud-ready applications, or a complex re-architecting of large legacy applications to cloud-supported technologies.
Business decision makers aiming for application modernization should note that only a few applications belong to either of these extremes. Most applications, written in modern technologies like Java and .NET, can be made suitable for cloud deployment by incorporating some changes. However, the task of making these applications cloud-ready turns out to be error-prone and effort-intensive due to the volume of changes pertaining to each component, and the large number of applications involved.
Refactoring involves restructuring the existing application with small changes while preserving its structure. The following changes need to be considered during refactoring:
1. Verifying and migrating the identified components, creating a build-ready and cloud-aware code, modifying the application code and configuration files to suit the target cloud environment, so that the benefits of cloud technologies can be harnessed effectively.
2. Upgrading the technology for programming language, middleware, database, and framework for aligning them with the application stack offered by the cloud vendor. For example, replacing the deprecated methods, annotations, libraries or keywords.
3. Modifying infrastructure dependencies, hard-coded topologies and configuration changes in the source application to ensure that they are compatible with the desired target environment.
For example, the IP address will have to be changed after moving to cloud, since a new IP/URI and MAC address will emerge.
Challenges in Implementing Application Refactoring
Reviewing and identifying changes in a large volume of files across all the applications of an enterprise while ensuring remediation of code and consistency of change, is a tiresome and complex process.
Manual interventions during code changes lead to considerable effort being spent on rectifying mistakes, rework, and manual check-out, modification and check-in cycles within version control systems.
The diversity of the target technology stack only adds to the complexity of this task. It requires varying changes to be made in the same application source code, so that different combinations of the target technology stack can be supported. This requires the highest level of automation, target technology expertise and experience.
Hence, technology upgrade with refactoring requires a product-based approach with a comprehensive understanding of the technical dependencies to meet end-user demands and speed up time to market.
Solutions Approach
The successful implementation of application refactoring can be achieved when enterprises find the right approach to facilitate highly automated solutions. The changes done to the existing code might have an impact on application functionality. This necessitates the involvement of the Quality Assurance team as an integral part of the refactoring process to minimize the overall cost and risks. The Solutions approach must also be used in combination with the right deployment team and tacit knowledge gained from each conversion engagement. An ideal product for refactoring should enable:
1. Planning the migration path with insights that capture the underlying complexity
2. Implementing rapid changes through impact analysis and remediation
3. Viewing the impact before the actual change is executed, and ensuring SME reviews to reduce manual intervention by developers later
4. Re-using and leveraging best practices to avoid repetitive errors
5. Making changes across different source code component types for different migration paths
A product-based approach aids in identifying impacted areas and remediates transformation in a highly automated and systematic way. Simultaneously, every deployment instance enhances the product’s knowledge repository as well as enables it to apply previous experiences for faster modernization implementations in the future.
Write to us at mastercraft.sales@tcs.com to start your application modernization journey.
Testing Transformation leveraging Process Optimization, Automation and Agility
Harnessing Generative AI in Procurement for Enhanced Efficiency
Enhancing Dealer Network Management with Master Data Management
Overcoming Barriers to Gen AI Adoption