How Refactoring Code Due to Changing Requirements Teaches 7 Valuable Lessons
IT Professors
How Refactoring Code Due to Changing Requirements Teaches 7 Valuable Lessons
Imagine a scenario where your carefully crafted code needs significant refactoring due to evolving requirements or a better grasp of the problem at hand. In this article, insights from an Expert Data Scientist and a Senior Software Engineer reveal the importance of modularizing pipelines for improved usability and the benefits of rigorous preparation to mitigate project risks. With a total of seven expert insights, this article provides a comprehensive look at best practices in code refactoring. The final insight emphasizes embracing iterative development for robust solutions.
- Modularize Pipeline for Improved Usability
- Rigorous Preparation Mitigates Risks in Projects
- Adapt Quickly to Changing Requirements
- Prioritize Simplicity in Code Design
- Test Thoroughly After Code Changes
- Communicate Effectively During Refactoring
- Embrace Iterative Development for Robust Solutions
Modularize Pipeline for Improved Usability
I created a machine learning system as part of an Inventory Management project to predict product demand and maximize inventory levels for a network of stores. In order to forecast weekly demand and determine reorder points, the system first employed a Random Forest model with simple sales, advertising, and weather data. In order to improve usability and trust, the client extended the scope halfway through to add dynamic supplier lead times, seasonal demand trends, and explainable forecasts. The codebase needed to be significantly refactored in order to accommodate these modifications. To ensure that data preprocessing, feature engineering, modeling, and inventory logic could all be changed separately, I modularized the pipeline. I used time-series data to improve feature engineering and shifted to an XGBoost model, which increased accuracy and included SHAP values for explainability, in order to capture seasonality and dynamic aspects. One of the most effective frameworks for analyzing machine learning models is SHAP (SHapley Additive exPlanations). Its foundation is cooperative game theory's Shapley values, which give each player in a game a number that corresponds to their contribution to the final result. To assist explain how much each feature affected the model's choice, SHAP assigns a contribution value to each feature for a particular prediction in the context of machine learning. SHAP highlighted the contributions of features such as "holiday season," "promotion," and "historical sales" in an inventory forecasting system to explain why the model forecasted a strong demand for a product in a particular week. This transparency makes it possible to spot biases or mistakes in the model and fosters trust with stakeholders. I also streamlined pipelines for scalability and consolidated inventory logic into a modifiable module. This refactor decreased late orders by 22%, increased forecast accuracy by 15%, and simplified system maintenance and expansion. I learned from the experience how crucial it is to prioritize scalability and explainability from the beginning, design systems for change, and have a thorough understanding of the problem domain.
Rigorous Preparation Mitigates Risks in Projects
Throughout my Principal Software Engineer career, I've encountered numerous situations where shifting requirements necessitated significant adjustments mid-project. One of the most recent experiences was leading the migration of half a company's business architecture to Cloud Kubernetes from On-Prem Windows VMs with a team of 30+ engineers.
Early on, the project faced challenges due to underestimating critical complexities, including insufficient research and brainstorming, gaps in QA, immature SDLC processes, weak CI/CD pipelines, and the intricacies of legacy systems with extensive dependencies and integration points. These issues revealed gaps in our acceptance criteria, requiring major refactoring and strategic pivots under tight deadlines.
Lessons:
1. The most important lesson was the value of rigorous preparation, encompassing comprehensive research, pilot implementations, and iterative analysis to mitigate risks in large-scale initiatives.
2. Equally, vital was fostering a collaborative culture with fast feedback loops, structured daily working sessions to resolve blockers or define clear next steps, strong ownership of tasks and challenging existing approaches.
3. This shift from reactive problem-solving to proactive, shared accountability resulted in exponential team growth, enhanced planning clarity, and seamless execution in future initiatives.
4. All that underscored the principle that success in complex projects lies not only in technical excellence but also from creating synergy by aligning people, processes, and priorities with a unified vision.
Adapt Quickly to Changing Requirements
When faced with changing requirements, the ability to adapt quickly becomes evident. Refactoring code under such conditions forces developers to think on their feet and make swift adjustments. This constant need for flexibility in their approach helps build strong problem-solving skills.
Adaptability in dynamic environments ensures that projects can continue to meet objectives despite new challenges. Strive to be adaptable in your coding practices to overcome future hurdles.
Prioritize Simplicity in Code Design
Keeping code simple is easier said than done, especially when requirements are constantly evolving. The process of refactoring highlights the necessity of simplicity in code design. Simple code is easier to maintain, update, and understand.
By prioritizing simplicity, developers can reduce the risk of introducing bugs during updates. Focus on creating straightforward solutions to maintain the integrity of your projects.
Test Thoroughly After Code Changes
Thoroughly testing code after any changes is essential to catch potential issues early. Refactoring due to new requirements emphasizes the importance of robust testing methods. Effective testing can uncover hidden problems that may not be obvious initially.
This practice ensures that the final product is reliable and performs well under various conditions. Always test thoroughly to ensure your code is both functional and dependable.
Communicate Effectively During Refactoring
Communicating effectively with team members becomes pivotal during refactoring projects. With changing requirements, everyone involved must be on the same page to ensure the smooth implementation of updates. Clear communication helps prevent misunderstandings and errors that could delay progress.
It also ensures that everyone understands the goals and reasons behind each change. Make communication a priority to facilitate successful project outcomes.
Embrace Iterative Development for Robust Solutions
Iterative development shows its strengths when refactoring code to meet new demands. Each iteration allows for gradual improvements and adjustments that make the solution more robust over time. By embracing iteration, developers can create software that stands up to evolving requirements efficiently.
This approach minimizes the risk of failure by allowing continuous testing and feedback. Adopt an iterative development mindset to enhance the durability of your solutions.