Strategies For Dealing With Unfamiliar Legacy Code In Scrum
Understanding the Challenges of Legacy Code
Legacy code presents multiple challenges for teams adopting Scrum practices. Often written years ago without tests or documentation, legacy codebases hamper developer productivity, increase defect rates, and slow the delivery of new features. Before integrating legacy code into sprints, teams must thoroughly assess its current quality and documentation levels to identify major pain points and risks.
Assessing Existing Code Quality and Documentation
A code assessment should gauge attributes like modularity, duplication, complexity, test coverage, and comment quality. Code with high duplication and complexity takes more time to understand and change. Lack of tests and documentation also impedes modification speed. teams can use static analysis and code climate tools like SonarQube and CodeScene to quantify code quality across various dimensions.
Identifying Major Pain Points and Risks
Legacy systems often have critical business logic deeply embedded across multiple modules. Teams must pinpoint major pain points – parts of the codebase with highest defect rates, longest modification times, and highest complexity. These areas pose the greatest risk of bugs and issues. Testing and documentation initiatives should focus on these components first. The assessment should also cover integration risks with other systems and capacity limitations that could constrain business initiatives.
Strategies to Incrementally Improve Legacy Code
Improvements to legacy codebases should start small and deliver value incrementally. Teams can pick targeted refactoring initiatives, add tests for critical paths, and improve documentation in stages. Each sprint should show some measurable progress like x% increase in test coverage or a decrease in average defect rate.
Adding Tests for Critical Paths
Tests protect against unintentional breaks when modifying complex code. Teams should prioritize adding unit and integration tests for critical business scenarios, highly used features, and frequently changing components. Tests prevent defects and enable faster parallel development. Teams can assess coverage with tools like Istanbul and Cobertura and add tests to reach a 60-70% target coverage over a few sprints.
Refactoring Modules with Highest Defect Rates
Code with many customer-impacting defects takes significant effort to maintain. Teams can analyze past bug reports and identify problem modules. Refactoring techniques like extracting functions, adding parameters, and simplifying conditional logic can then target these modules. Small, incremental refactoring combined with tests accelerates future development.
Improving Documentation and Comments
Good documentation speeds up knowledge transfer and shortens issue resolution time. Teams should focus first on architectural diagrams, data models, interface specifications and high-level component overviews. Inline comments explaining core logic and complex sections also help new engineers quickly understand code function. Teams can assign documentation tasks each sprint to build useful artifacts.
Integrating Legacy Code into a Scrum Process
Bringing uncontrolled legacy code into a Scrum process requires changes to sprint planning, cross-functional involvement, and approaches to technical debt. Teams must create room for learning, experimentation and incremental improvements within time-boxed sprints.
Planning Work on Legacy Code into Sprints and Releases
Breaking down amorphous tasks like “understand system X” or “improve module Y” into actionable stories lets teams make reliable sprint commitments. Stories can cover adding tests, writing documentation, or targeting localized refactoring. Planning legacy code tasks alongside new feature work also ensures technical improvements actually happen and don’t get continuously deferred.
Assigning Ownership and Responsibilities
Shared code ownership avoids knowledge silos and distributes costs of maintenance. Teams should establish broad write access and norms encouraging contributions regardless of functional domain. Automated testing and peer-review processes give the team confidence that changes don’t introduce issues. Collective ownership builds experience with different components.
Allowing Time for Learning and Experimentation
Working with unfamiliar legacy code slows teams down as they learn various intricacies. Sprints should account for these early inefficiencies with reduced velocity targets. Teams can also designate spike stories for targeted prototyping and discovery tasks needed to build knowledge. These experiments inform technical approach decisions for subsequent sprints.
Measuring Progress and Success
Quantifiable metrics let teams assess if legacy code efforts actually achieve intended improvements sprint-over-sprint. Useful metrics capture code quality, defects, productivity, and agility outcomes.
Defining Metrics for Code Quality
Useful code quality metrics include test coverage for risk mitigation, code duplication to gauge improvement opportunities, cyclic complexity to measure understandability, and technical debt principal for the refactoring backlog. Teams should define target levels and track trends over multiple sprints.
Tracking Reductions in Defects and Technical Debt
The ultimate goals are to reduce production defects and technical debt. Teams can measure rolling average defect rates by priority level and component. Technical debt metrics cover principal paid back through refactoring and total estimated accumulated debt. These indicate whether quality improvement initiatives translate to tangible improvements.
Gauging Team Efficiency and Productivity
Velocity, lead time, throughput, and productivity metric trends reveal whether familiarity with the legacy codebase improves over time. Comparing output between teams with and without legacy code also informs planning and investment decisions.
Key Takeaways and Best Practices
Succeeding with legacy code in Scrum requires an incremental, measurement-driven approach focused on business risks. Teams should emphasize adding tests, documenting architectural layers, establishing collective ownership, and targeting technical debt reduction. Each sprint should yield measurable improvements in code quality, defects, productivity, or agility. Steady quantifiable progress builds business confidence in supporting increased investments.
