Comparer des méthodes
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| Métriques de complexité logicielle× | Analyse de la couverture de code× | |
|---|---|---|
| Domaine | Génie logiciel | Génie logiciel |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1976 | 1988 |
| Auteur d'origine≠ | Thomas J. McCabe | Test Coverage Community |
| Type≠ | quantitative measurement | measurement and analysis |
| Source fondatrice≠ | McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320. DOI ↗ | Zhu, H., Hall, P. A. V., & May, J. H. R. (1997). Software unit test coverage and adequacy. ACM Computing Surveys, 29(4), 366–427. DOI ↗ |
| Alias≠ | code complexity analysis, complexity measurement | coverage metrics, test coverage, instrumentation-based measurement |
| Apparentées | 4 | 4 |
| Résumé≠ | Software complexity metrics quantify the structural and operational difficulty of code through numerical measurements. Introduced by Thomas McCabe in 1976, cyclomatic complexity became the foundational approach. These metrics assess maintainability, testability, and defect risk, enabling teams to identify problematic code regions and guide refactoring efforts. | Code coverage analysis measures the extent to which source code is executed by a test suite, quantifying which lines, branches, or paths are exercised. Tools instrument code to track execution, reporting coverage percentages and identifying untested regions. Coverage analysis guides test creation, detects dead code, and validates test adequacy in quality assurance processes. |
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