How can you balance quality and speed when working with legacy code?

Balancing quality and speed when dealing with legacy code requires a strategic approach. Firstly, prioritize critical areas for improvement based on impact analysis. Implement automated testing to catch regressions and ensure code stability. Utilize refactoring techniques to gradually enhance code quality without sacrificing speed. Lastly, foster a culture of collaboration and knowledge sharing to empower teams in navigating and modernizing legacy systems efficiently.

Working with a legacy product is an extremely challenging task, especially when you are new to the organization or product. I worked for a product for around 12 years, starting in 2000. The product has been in existence since the 1980s, and hundreds of developers have worked on it before. That essentially meant 1) Array of legacy technologies and languages being used in codebase 2) Availability of documentation 3) Lots of integration 4) Huge codebase and large feature set 5) Patches and workarounds Having said that, there were also good things that helped 1) Strong code review practices 2) Unit, acceptance and automation testing 3) Decent documentation 4) Refactoring of legacy code in every release 5) above all, a desire to modernize code

Understanding the codebase is akin to unraveling a complex puzzle, a lesson learned firsthand during my tenure with a legacy project. While tasked with implementing new features, diving into the labyrinthine codebase revealed a myriad of interconnected modules and arcane logic. Through meticulous examination aided by code analysis tools and documentation, I deciphered the cryptic architecture, unraveling its intricacies. Collaborating with seasoned developers and leveraging their insights proved invaluable, shedding light on obscure dependencies and hidden pitfalls.

Understand the codebase. Prioritize high-impact areas. Set realistic goals. Refactor incrementally. Automate testing. Use tools and libraries. Involve stakeholders. Document changes. Iterate and adapt. Seek continuous improvement.

Speed and quality are both equally important, but I will say that speed becomes secondary if results are not reliable. There is a thin line that separates the two. For legacy codebases, we need to make dedicated effort to identify the scope as well as priorities. Adding new features to new releases demands that we refactor code, modernize the design, and also ensure integrations do not break, which is essentially about ensuring quality and speed. The more the codebase grows, the slower it becomes. Thus it boils down to 1) What do you want to develop? 2) How do you develop it? 3) How do you balance new features, speed, and quality? 4) What are the constraints in terms of scope, schedule and cost? 5) What is your delivery model?

Identify Core Functionality: Clearly define the critical functionalities of the legacy code that must be maintained or improved. Focus on these areas first to ensure core operations remain stable. Risk Assessment: Analyze potential risks associated with code changes. Prioritize addressing critical bugs and security vulnerabilities over less urgent improvements. Phased Approach: Break down the project into smaller, manageable phases. This allows for quality checks and adjustments after each phase, reducing the risk of introducing regressions.

If we do not refactor the legacy code, the product would die sooner than later. In my case, we kept extending the life cycle of our product by active refactoring of our code base to 1) Modernize per new tech stack 2) Modular design: we moved from C to object oriented languages, modernized design using SOLID principles and design patterns 3) We added lot of automation and unit testing 4) We laid lot of emphasis on code review process, style guides, code formatting and intellisense tools

Consider avoiding monolithic refactoring initiatives as regression risks and business interruptions often overcome benefits; let instead highest-priority new features (or defects) guide targeted incremental reworks: as soon as a problematic piece of legacy code is encountered in the development, leave it better than you found it. This approach has been described by Martin Fowler as "Litter-Pickup Refactoring" and, in my own experience, has proven to be the most versatile and balanced approach to improve an existing codebase without disrupting both entrepreneurial and technical expectations.