Files and links (2)
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0, Open
Conference proceeding
M-score: An Empirically Derived Software Modularity Metric
ESEM '24: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pp 382-392
24 Oct 2024
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
Background: Software practitioners need reliable metrics to monitor software evolution, compare projects, and understand modularity variations. This is crucial for assessing architectural improvement or decay. Existing popular metrics offer little help, especially in systems with implicitly connected but seemingly isolated files.
Aim: Our objective is to explore why and how state-of-the-art modularity measures fail to serve as effective metrics 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 for 1,220 releases across 37 projects to identify the root causes of their shortcomings. This led to the creation of M-score, a new software modularity metric that combines the strengths of existing metrics while addressing their flaws. M-score rewards small, independent modules, penalizes increased coupling, and treats isolated modules and files consistently.
Results: Our evaluation revealed that M-score outperformed other modularity metrics in terms of stability, particularly with respect to isolated files, because it 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.
Metrics
33 Record Views
Details
- Title
- M-score: An Empirically Derived Software Modularity Metric
- Creators
- Yuanfang Cai (Corresponding Author) - Drexel University, Computer ScienceErnest Pisch - Drexel UniversityRick Kazman - University of Hawaii SystemJason Lefever - Drexel UniversityHongzhou Fang - Drexel University
- Publication Details
- ESEM '24: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pp 382-392
- Conference
- ESEM '24: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (Barcelona, Spain, 24 Oct 2024–25 Oct 2024)
- Publisher
- Association for Computing Machinery
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-85210567123
- Other Identifier
- 991021906502704721