Legacy System Modernization

We audit the existing codebase and identify the amount of technical debt, code complexity, test coverage, dependency situation, and places in the codebase that prevent safe changes. The result is the written report.

We evaluate the system’s architecture in search of scaling limitations, strong coupling between modules and system components, and design patterns that reduce the velocity of software development and/or make the system hard to extend. The result is the written architecture review and architectural improvement suggestions.

We create a plan of modernization in advance, which includes defining its scope, modernization approach, and a roadmap to achieve desired results, and covers all necessary levels of the process (code, architecture, and data modernization) and the effort estimate for each.

Legacy Code Refactoring We refactor existing application codebases and remove accumulated technical debt in a way that does not influence the external system’s functionality by eliminating duplicated code, simplifying code structure, and organizing code in a better way.

We audit and update the codebase by eliminating deprecated and unmaintained libraries, removing SDKs that are out of date, and upgrading framework versions. The main focus of the work is code maintainability.

We provide the implementation of unit, integration, and regression testing suites when a system has no automatic test coverage and/or the coverage is incomplete. The test coverage is the basis for successful refactoring and any modifications.

We redesign legacy user interfaces by rebuilding them using the current frameworks while leaving business logic and data model untouched.

We use AI-assisted tools to improve and speed up modernization by means of automated code analysis, generating documentation for an undocumented codebase, creating tests for it, and refactoring complex legacy code.

We refactor monolithic apps to modules within one system to enhance maintenance, facilitate development process flows, and allow independent working for team members without necessarily moving to a distributed environment.

We transform existing architecture into independently deployable microservices, which allows for scalability, independence in scaling and integrating, etc.

We add or completely redesign the API layer in cases when the architecture of the system allows direct access to business logic via UI, or there is no documented public interface. The API layer provides loose coupling of the consuming applications with the backend and allows extensibility.

We use event-driven patterns of communication between modules to improve the resilience of the system and decrease inter-module coupling in places where it makes sense, according to our findings.

The purpose of this step is to optimize the database schema of the system by removing duplicate tables, denormalizing or normalizing the schema, removing undocumented procedures, and optimizing indexes.

The task is to replace old data access layers with up-to-date implementations, including updating ORM mapping if necessary.

The step includes checking and resolving data quality and integrity problems within legacy databases (duplicates, invalid data, orphans).

We establish a continuous integration/deployment pipeline for building and deploying applications if such a process is absent or partly automated currently.

In addition to unit tests, the system will benefit from the implementation of an automated testing pipeline with code coverage, static analysis, and other tools to check the quality of the code.

Our company will provide maintenance and bug fixing services for legacy and updated applications, and database management services for the database environment used by the applications.