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Challenges in software model reuse: cross application domain vs. cross modeling paradigm

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Abstract

Software reuse is a common practice that aims to reduce costs and effort, while improving quality and productivity. However, it also raises challenges of retrieving existing artifacts and adapting them to the given context. Sometimes, the most relevant artifacts are realized in a different application domain and/or in a different paradigm (e.g., object-oriented vs. data-driven). These challenges are extremely relevant to non-code artifacts, such as models, which are relatively rare, as well as vary in the level of details and quality. In this paper, we aim to explore the challenges and opportunities of cross application domain and cross modeling paradigm model reuse. These types of reuse require different mapping mechanisms (analogy creation and transformation, respectively), but similar adaptation operations (use-as-is, modification, omission and addition). To explore the challenges of these reuse types, we present the design and the results of a series of controlled experiments, involving 64 participants, which analyzed correctness of software model reuse across application domains and across two modeling paradigms: object-oriented expressed in UML use case and class diagrams and data-driven expressed in entity-relationship and data flow diagrams. Our results show that overall cross-domain reuse is more correctly performed than cross-paradigm reuse, especially with respect to addition. We further analyzed the challenges in each reuse type and found that modification and addition in both reuse types are quite challenging and require careful support to meet new or differing requirements.

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Data Availability

The full experimental material associated with the current study is available in the Zenodo repository, https://zenodo.org/record/7565599.

Notes

  1. Here differences in terminologies may also challenge the transformation. However, for simplicity, we assume that the single domain involved in cross-paradigm reuse is stable and has an agreed terminology. Further research is needed to explore and analyze more complicated cases, in which there is no single underlying terminology.

  2. Based on the wide existing literature on the reuse benefits on quality, the experiment does not include a third (control) group – no reuse, as our purpose is to compare the challenges and opportunities of specific reuse types.

  3. Approved by The Institutional Review Board (IRB) at the University of Haifa, Israel; Application no. 492/21.

  4. The full experimental material is available at https://zenodo.org/record/7565599. Examples of source and target models, as well as possible mappings, are depicted in Figs. 1 and 2.

  5. According to (Santos et al. 2019), IPD-S increases the interpretability of joint results and offers greater statistical flexibility.

  6. A 95% confidence interval is a range of values above and below the point estimate within which the true value in the population is likely to lie with 95% confidence.

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  1. Department of Information Systems, University of Haifa, 3498838, Haifa, Israel

    Iris Reinhartz-Berger

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  1. Iris Reinhartz-Berger
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Correspondence to Iris Reinhartz-Berger.

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The author declared that she has no conflict of interest. The experiments comply with all international standards for ethical research practice (see details in footnote #3).

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Communicated by: Bram Adams

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Reinhartz-Berger, I. Challenges in software model reuse: cross application domain vs. cross modeling paradigm. Empir Software Eng 29, 16 (2024). https://doi.org/10.1007/s10664-023-10386-9

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