In automotive industry, more and more complex electronics and software systems are being developed to enable the innovation and to decrease costs. Besides the complex multimedia, comfort, and safety systems of conventional vehicles, automotive companies are required to develop more and more complex engine, aftertreatment, and energy management systems for their (hybrid) electric vehicles to reduce fuel consumption and harmful emissions. MATLAB/Simulink is one of the most popular graphical modeling languages and a simulation tool for validating and testing control software systems. Due to the increasing complexity and size of Simulink models of automotive software systems, it has become a necessity to maintain the Simulink models.In this paper, we defined metrics for assessing the modularity of Simulink models. A Java tool developed to measure the defined metrics on Simulink models interfaces with a visualization tool to facilitate the maintenance tasks of the Simulink models. The modularity metrics is furthermore validated in two phases. In the first phase, the modularity measurement is validated against the experts evaluation of a system. In the second phase, we studied the relationship between metric values and number of faults. We have observed that high coupling metric values frequently correspond to number of faults. Modularity metrics will be extended to architectural quality metrics for automotive systems.

Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. ABSTRACTThe size and complexity of Simulink models is constantly increasing, just as the systems which they represent. Therefore, it is beneficial to control them already at the design phase. In this paper we establish a set of complexity metrics for Simulink models to capture diverse aspects of complexity by proposing new and redefining existing metrics. To evaluate the applicability of our metrics, we compare them with the closed-source metric proposed by Mathworks. Moreover, through a case study from the automotive domain, we relate such metrics to quality attributes as determined by domain experts, and correlate them to known faults. Preliminary assessment suggests that complexity is closely related to analysability, understandability, and testability.

In this paper, we present a novel industry dataset on static software and change metrics for Matlab/Simulink models and their corresponding auto-generated C source code. The data set comprises data of three automotive projects developed and tested accordingly to industry standards and restrictive software development guidelines. We present some background information of the projects, the development process and the issue tracking as well as the creation steps of the dataset and the used tools during development. A specific highlight of the dataset is a low measurement error on change metrics because of the used issue tracking and commit policies.

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