Abstract
The primary purpose of software design is to reduce the complexity of software systems and to abstract the details to aid in comprehension. Design languages, such as UML, offer increased flexibility and alternatives for designers. However, there are no clear objective guidelines to help software designers select alternatives that reduce model complexity and improve their comprehensibility. Current model complexity metrics are often dependent exclusively on the number of model elements without appropriately considering the complexity of these elements and their interactions. In this paper, we introduce novel complexity metrics for UML state machines models. The proposed approach and metrics are formulated by estimating a complexity rate for each element in the state machine model. The paper presents an in-depth theoretical validity evaluation using Briand ́s theoretical property-based framework to ensure the correctness of the metrics. The proposed complexity metrics satisfy four of the five Briand’s properties.
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Masmali, O., Badreddin, O. (2021). Theoretically Validated Complexity Metrics for UML State Machine Models. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3. FTC 2020. Advances in Intelligent Systems and Computing, vol 1290. Springer, Cham. https://doi.org/10.1007/978-3-030-63092-8_28
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