Legacy Modernization

Common application modernization challenges

Take a look at these 8 top challenges faced when modernizing a legacy system:

• Complexity and dependencies: Dealing with intricate architectures and interdependencies in legacy applications.
• Skill gap: Finding developers skilled in both legacy and modern technologies.
• Legacy hardware and infrastructure: Challenges arising from applications tied to specific, outdated hardware or infrastructure.
• Risk of business disruption: Potential operational disruptions during the modernization process.
• Cost and budget constraints: Managing substantial costs within budget limitations.
• Time constraints: Balancing the need for modernization with deadlines for new features or updates.
• Security concerns: Addressing security vulnerabilities that might be introduced during modernization.
• Compatibility issues: Ensuring the modernized application integrates seamlessly with existing systems and third-party applications.

How to deal with technical debt

To effectively manage technical debt, it's important to:

• Identify and measure debt: Recognize signs of technical debt and assess the time required for its reduction.
• Prioritize and plan: Set coding standards and schedule periods for debt repayment.
• Refactor and test: Regularly refactor the code and conduct automated tests.
• Promote code reviews: Implement a culture of thorough code reviews and continuous testing.
• Manage through Agile approaches: Utilize Agile methodologies to handle technical debt proactively.

How to prioritize tech debt repayment

Use this process to prioritize technical debt:

• Identify and understand the debt: Recognize areas of debt and their impact.
• Assess the risk: Evaluate the risk associated with each debt.
• Consider the business impact: Analyze how the debt affects business operations.
• Prioritize based on cost-benefit analysis: Weigh the costs of repayment against the benefits.
• Create a repayment plan: Develop a strategic plan for addressing the debt.

Technical debt metrics

There are various metrics that allow development teams to track technical debt effectively. For example:‍

Code Churn:

Code churn refers to how often and how extensively the codebase changes over a certain period. It is measured by tracking the additions, deletions, and modifications of code lines.

High code churn can indicate instability or constant rework in the code, which may contribute to technical debt. Regularly measuring code churn helps in understanding the stability and maturity of the software, guiding decisions on where to focus development efforts to reduce unnecessary rework and inefficiencies.

Cyclomatic Complexity:

Cyclomatic complexity is a metric used to determine the complexity of a program by measuring the number of linearly independent paths through the program's source code.

A higher value indicates more complex code, which can be harder to understand, test, and maintain, thereby increasing technical debt. Regular evaluation of cyclomatic complexity helps in identifying overly complex sections of code that might need refactoring to simplify and reduce associated technical debt.

Technical Debt Ratio:

The technical debt ratio compares the cost of fixing technical debt (e.g., refactoring code, fixing bugs) to the cost of developing the code. It provides a quantitative measure of the amount of technical debt in a project relative to its size. This ratio helps in understanding the scale of technical debt and can inform prioritization and resource allocation for debt reduction efforts.

Code Coverage:

Code coverage measures the percentage of the codebase that is tested by automated tests (like unit tests, integration tests).

High code coverage generally indicates a lower likelihood of undetected bugs and issues in the code, which can contribute to technical debt. Measuring code coverage helps in identifying areas of the code that are under-tested and thus more prone to errors, allowing teams to target these areas for improved testing and quality assurance.

Static Code Analysis Issues:

Static code analysis tools scan code for issues such as coding standard violations, potential bugs, security vulnerabilities, and code smells. Tracking the number and severity of issues identified by these tools provides insights into the quality of the codebase.

A high number of critical issues can indicate significant technical debt, requiring immediate attention. Continuous monitoring of static code analysis issues helps in maintaining code quality and reducing the buildup of technical debt over time.

Remember to track the metrics that are relevant to your case, experiment, and avoid tracking only one metric to prevent false results.

Tools to measure technical debt

You can measure technical debt with some of the following tools:

• SonarQube: An open-source platform for continuous inspection of code quality.
• Jira Software: A tool for issue and project tracking that can be adapted to track technical debt.
• Squore: A software analytics tool that provides insights and metrics for technical debt.
• NDepend: A static analysis tool for .NET applications that includes technical debt estimation.
• Cast Software: An application intelligence platform offering comprehensive analysis for technical debt.

Technical Debt Ratio: Why & how to calculate

The Technical Debt Ratio is a metric that compares the cost of fixing code issues to the cost of developing the code. It's an important measure because it quantifies the amount of technical debt in a project, allowing for a better understanding of the project's quality. 

By knowing the ratio, teams can make informed decisions about whether to address technical debt immediately or delay it, based on the potential impact on the project's future development and maintenance costs.

How to calculate?

Calculate the cost to develop the existing code (C_dev):

This can be estimated by multiplying the total number of lines of code (LOC) in your project by the average cost per line of code. The cost per line can vary based on factors such as the programming language used, the complexity of the code, and the geographical location of the developers.

Estimate the cost to fix the technical debt (C_debt):

This involves identifying issues in the code that contribute to technical debt, such as bugs, code smells, duplications, and violations of coding standards. Tools like SonarQube, JIRA, or other static code analysis tools can help identify these issues.Once identified, estimate the time required to fix these issues and then multiply this time by the average hourly rate of your development team to get the cost.

Calculate the Technical Debt Ratio:

Technical Debt Ratio = C dev/C debt×100

This ratio is expressed as a percentage. A higher percentage indicates a higher level of technical debt relative to the total development cost.

Technical debt examples and how to deal with them

Take a look at these 5 examples of technical debt and learn how to deal with each of them:

Outdated libraries

Using older versions of libraries that may lack support or have security vulnerabilities.

How to deal: Regularly update libraries to newer versions to enhance support and security.

Lack of documentation

Missing or outdated documentation that makes understanding and modifying code difficult.

How to deal: Create and maintain comprehensive, up-to-date documentation for better code understanding and ease of modifications.

Code duplication

Repeated code blocks that increase maintenance effort and bug risks.

How to deal: Refactor code to eliminate duplications, enhancing maintainability and reducing bug risks.

Lack of testing

Insufficient automated tests leading to potential undetected issues in the code.

How to deal: Implement robust automated testing to catch issues early and ensure code reliability.

Complex code

Unnecessarily complex or poorly structured code that is hard to maintain and understand.

How to deal: Simplify and restructure code for easier maintenance and comprehension.

Big bang migration vs incremental approach (Trickle migration)

Big Bang Migration involves transferring all components of a system to a new environment at once, usually executed in a short, intensive period. It's suitable for smaller or less complex systems where downtime is manageable. 

Trickle Migration, on the other hand, involves gradually moving parts of a system over time, minimizing disruptions but requiring more complex coordination. It's preferred for larger, complex systems where maintaining operations during the transition is crucial.

Blue/green deployment for legacy system modernization

Blue/Green Deployment is a strategy for updating applications with minimal downtime and risk. 

It works by maintaining two identical production environments: the 'Blue' (active) and 'Green' (inactive). New versions are deployed to the Green environment, tested, and once verified, traffic is switched from Blue to Green.

It's especially useful in environments where continuous uptime is critical, as it allows for immediate rollback if issues arise with the new version.

Data migration strategy - step-by-step process

• Comprehensive data assessment: Carefully analyze the data in the existing system, focusing on quality, size, dependencies, and data formats.
• Clear migration objectives: Establish well-defined, quantifiable targets for the migration in line with overall business goals.
• Detailed project planning: Create a comprehensive plan covering the project's scope, duration, resources, budget, and major milestones.
• Choosing the right tools and technologies: Opt for migration tools and technologies suitable for both old and new systems.
• Data mapping and integrity checks: Plan the transition of data from the old to the new system, ensuring data integrity.
• Risk management strategy: Identify and prepare for possible risks and challenges.
• Testing and validation: Conduct extensive testing at all stages of migration to verify data correctness and system performance.
• Post-migration review and support: Perform an in-depth review after migration and provide continuous support and training to users.

The Strangler Pattern approach to legacy modernization

The Strangler Pattern is a strategy for modernizing legacy systems incrementally. It involves building a new system around the edges of the existing one and gradually replacing old components. 

This method is ideal for large-scale systems where a complete overhaul is too risky or disruptive. It allows for steady progress, continuous integration of new features, and testing without a complete system shutdown. This approach is particularly beneficial when dealing with complex, monolithic applications that require modernization without affecting ongoing business operations.

Monolith to microservices using Strangler Pattern

10 phases of migration from monolith to microservices

1. Identify and isolate components

2. Build the Strangler facade

3. Design and develop microservices

4. Build data migration and management strategy

5. Incrementally replace functionalities

6. Test each microservice

7. Set up Continuous Deployment and Integration

8. Gradually decommission the monolith

9. Monitor and optimize performance

10. Update documentation and share knowledge

Top data migration tools

1. Informatica PowerCenter
2. Talend Data Integration
3. Oracle Data Integrator (ODI)
4. IBM InfoSphere DataStage
5. SAP Data Services
6. Microsoft SQL Server Integration Services (SSIS)
7. AWS Data Migration Service (DMS)
8. Google Cloud Dataflow
9. Azure Data Factory
10. Fivetran

How to avoid technical bankruptcy

Technical bankruptcy occurs when accumulated technical debt severely impacts a system's functionality or a company's operations. 

To avoid technical bankruptcy:

• Maintain continuous code quality and standards.
• Regularly refactor and update the codebase.
• Prioritize technical debt reduction in project planning.
• Ensure adequate documentation and knowledge sharing.
• Adopt a proactive approach to address issues early.

How to create a legacy app modernization roadmap