This chapter introduces an enhanced framework for prioritizing the refactoring of data clumps in software projects, incorporating new quantitative criteria based on length and similarity measures. These additions, alongside existing factors like Widespread Occurrence, Size, and Dependency, improve the precision of the refactoring process. Advanced similarity measures, such as Cosine Similarity and Levenshtein Distance, enhance the detection and prioritization of critical data clumps. Experimental evaluations on real-world datasets demonstrate the framework’s effectiveness, showing that incorporating these new metrics provides a nuanced understanding of code complexity, leading to more targeted and efficient refactoring efforts.