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An approach to improve the quality of object-oriented models from novice modelers through project practice

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Abstract

The defects in object-oriented models will result in poor quality of applications based on the models, and thus it is necessary to know which defects often occur in practice, to what extent they occur, why they occur, and how they can be prevented. To gain deeper insights into these problems, this paper discusses how to improve the quality of object-oriented models from novice modelers through project practice. This paper summarizes a set of typical quality defect types from a large number of the defects, and confirms them through our project practice. Moreover, the paper analyzes the improvement of the quality of object-oriented models by quantifying the level of occurrence for the defect types in different phases of the project practice, and presents preventive measures by analyzing the causes for the defects to occur in object-oriented models in the aspects of syntax, semantics, and pragmatics.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61272159 and 61672046).

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Authors and Affiliations

  1. Software Institute, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Zhiyi Ma

  2. Key laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, 100871, China

    Zhiyi Ma

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  1. Zhiyi Ma
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Correspondence to Zhiyi Ma.

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Zhiyi Ma received his PhD in computer science in 1999. His research interests focus on software modeling methods, metamodeling technology, and software engineering environments. He is the author of twelve books and more than 100 publications in journals and conferences. He received the China National Science and Technology Progress second award and several special contribution awards of China National Ministries and Commissions.

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Ma, Z. An approach to improve the quality of object-oriented models from novice modelers through project practice. Front. Comput. Sci. 11, 485–498 (2017). https://doi.org/10.1007/s11704-016-5164-8

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