To ensure that the logical constructs within the code are tested.
- Méthodologies : Ingénierie, Conception de Produits, Gestion de projet
Test de couverture logique

Test de couverture logique
- Amélioration continue, Assurance qualité, Contrôle de qualité, Gestion des risques, Ingénierie logicielle, Software Testing, Méthodes d'essai, Validation, Vérification
Objectif :
Comment il est utilisé :
- A white-box testing technique that focuses on the execution of logical statements (e.g., AND, OR, XOR) within the code. It involves creating test cases to ensure that all possible outcomes of logical conditions are tested.
Avantages
- Provides a high level of test coverage; Can uncover errors in complex logical expressions.
Inconvénients
- Can be difficult and time-consuming to achieve 100% coverage; Does not test for errors outside of logical expressions.
Catégories :
- Ingénierie, Qualité
Idéal pour :
- Testing safety-critical software where logical errors could have severe consequences.
Logic-Coverage Testing is particularly valuable in industries where safety is paramount, such as aerospace, automotive, and healthcare, where software failures can lead to catastrophic consequences. This methodology is often employed in the verification phase of software development, especially for systems that rely heavily on intricate logic and decision-making processes. Engineers and testers, including software developers and quality assurance teams, typically initiate this testing approach by analyzing code paths and identifying logical statements that must be executed to ensure complete functional validation. It can be paired with formal verification techniques to increase confidence in the reliability of systems, especially where regulatory standards impose stringent compliance requirements. For example, in the automotive industry, Logic-Coverage Testing could be applied to electronic control units (ECUs) that manage engine performance, where even the smallest error in logic could compromise safety. In healthcare, medical devices running complex algorithms, such as infusion pumps or diagnostic devices, can be subjected to this testing to prevent potential malfunctions that could endanger patient safety. The method can reveal hidden logical conditions that may not be evident through traditional testing approaches, thus improving software robustness. Although it requires significant time and resources to design comprehensive test cases, the high level of coverage achieved allows for greater assurance that all logical branches have been evaluated, mitigating risks associated with undetected software errors.
Principales étapes de cette méthodologie
- Identify all logical statements in the code that can be tested.
- Determine the possible outcomes for each logical statement.
- Create test cases that cover each outcome of every logical condition.
- Execute the test cases and observe the results against expected outcomes.
- Analyze the results to identify any logical errors based on the execution paths taken.
- Refine test cases as necessary to ensure all logical branches have been tested.
- Repeat testing until all logical conditions are reached with satisfactory coverage.
Conseils de pro
- Utilize decision tables to systematically cover all combinations of input conditions, ensuring exhaustive testing of logical outcomes.
- Incorporate mutation testing to verify that the tests effectively identify logical flaws by introducing small changes to the code and checking if tests fail as expected.
- Leverage code coverage tools that can pinpoint untested logical branches specifically, enabling targeted refinement of test cases for improved reliability.
Lire et comparer plusieurs méthodologies, nous recommandons le
> Référentiel méthodologique étendu <
ainsi que plus de 400 autres méthodologies.
Vos commentaires sur cette méthodologie ou des informations supplémentaires sont les bienvenus sur le site web de la Commission européenne. section des commentaires ci-dessous ↓ , ainsi que toute idée ou lien en rapport avec l'ingénierie.
Articles Similaires
Calculateur de METS en calories
Méta-analyse
Cartographie des messages
Diagrammes du modèle mental
Forces de poussée et de traction maximales acceptables
Planification des besoins en matériaux (MRP)