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UML models consistency management: Guidelines for software quality manager

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  • Bashir, Raja Sehrab
  • Lee, Sai Peck
  • Khan, Saif Ur Rehman
  • Chang, Victor
  • Farid, Shahid

Abstract

Unified Modeling Language (UML) has become the de-facto standard to design today’s large-size object-oriented systems. However, focusing on multiple UML diagrams is a main cause of breaching the consistency problem, which ultimately reduces the overall software model’s quality. Consistency management techniques are widely used to ensure the model consistency by correct model-to-model and model-to-code transformation. Consistency management becomes a promising area of research especially for model-driven architecture. In this paper, we extensively review UML consistency management techniques. The proposed techniques have been classified based on the parameters identified from the research literature. Moreover, we performed a qualitative comparison of consistency management techniques in order to identify current research trends, challenges and research gaps in this field of study. Based on the results, we concluded that researchers have not provided more attention on exploring inter-model and semantic consistency problems. Furthermore, state-of-the-art consistency management techniques mostly focus only on three UML diagrams (i.e., class, sequence and state chart) and the remaining UML diagrams have been overlooked. Consequently, due to this incomplete body of knowledge, researchers are unable to take full advantage of overlooked UML diagrams, which may be otherwise useful to handle the consistency management challenge in an efficient manner.

Suggested Citation

  • Bashir, Raja Sehrab & Lee, Sai Peck & Khan, Saif Ur Rehman & Chang, Victor & Farid, Shahid, 2016. "UML models consistency management: Guidelines for software quality manager," International Journal of Information Management, Elsevier, vol. 36(6), pages 883-899.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:6:p:883-899
    DOI: 10.1016/j.ijinfomgt.2016.05.024
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    References listed on IDEAS

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    1. Larson, Deanne & Chang, Victor, 2016. "A review and future direction of agile, business intelligence, analytics and data science," International Journal of Information Management, Elsevier, vol. 36(5), pages 700-710.
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