“Background: Software practitioners need reliable metrics to monitor software evolution, compare projects, and capture modularity variations. This is crucial for assessing architectural improvement or decay. Popular existing metrics are of little help here, especially in systems with implicitly interconnected but seemingly isolated files. Aim: Our objective is to explore why and how state-of-the-art modularity measures fail to serve as an effective metric and to devise a new metric that more accurately captures complexity changes and is less distorted by sizes or isolated files. Methods: We analyzed metric scores from 1,220 releases across 37 projects to understand the root causes of their shortcomings and the effects of isolated files. These insights lead to the creation of a new software modularity metric: M-score. M-score combines the best aspects of two existing metrics and addresses their problems. It rewards small, independent modules, imposes penalties for increased coupling within and between them, and treats isolated modules and files uniformly. Results: Our evaluation revealed that M-score outperformed other modularity metrics in terms of stability, particularly with respect to isolated files, because it mainly captures coupling density and module independence. It also correlated well with maintenance effort, as indicated by historical maintainability measures, meaning that the higher the M-score, the more likely maintenance tasks can be accomplished independently and in parallel. Conclusions: Our research identifies the shortcomings of current metrics in accurately depicting software complexity and proposes M-score, a new metric with superior stability and better reflection of complexity and maintenance effort, making it a promising metric for software architectural assessments, comparison, and monitoring.”