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MUPPIT: a method for using proper patterns in model transformations

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Abstract

Model transformation plays an important role in developing software systems using the model-driven engineering paradigm. Examples of applications of model transformation include forward engineering, reverse engineering of code into models, and refactoring. Poor-quality model transformation code is costly and hard to maintain. There is a need to develop techniques and tools that can support transformation engineers in designing high-quality model transformations. The goal of this paper is to present a process, called MUPPIT (method for using proper patterns in model transformations), which can be used by transformation engineers to improve the quality of model transformations by detecting anti-patterns in the transformations and automatically applying pattern solutions. MUPPIT consists of four phases: (1) identifying a transformation anti-pattern, (2) proposing a pattern-solution, (3) applying the pattern-solution, and (4) evaluating the transformation model. MUPPIT takes a transformation design model (TDM), which is a representation of the given transformation, to search for the presence of an anti-pattern of interest. If found, MUPPIT proposes a pattern solution from a catalogue of patterns to the transformation engineer. The application of the pattern solution results in the restructuring of the TDM. While MUPPIT, as a process, is independent of any transformation language and transformation engineering framework, we have implemented an instance of it as a tool using transML and MeTAGeM, which support exogenous transformations using rule-based transformation and OCL-based languages such as ATL and ETL. We evaluate MUPPIT through a number of case studies in which we show how MUPPIT can detect four anti-patterns and propose the corresponding pattern solutions. We also evaluate MUPPIT by collecting a number of metrics to assess the quality of the resulting transformations. The results show that MUPPIT optimizes the transformations by improving reusability, modularity, simplicity, and maintainability, as well as decreasing the complexity. MUPPIT can help transformation engineers to produce high-quality transformations using a pattern-based approach. An immediate future direction would be to experiment with more anti-patterns and pattern solutions. Moreover, we need to implement MUPPIT using other transformation engineering frameworks.

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Notes

  1. Cuadrado et al. [12] believe that any can be used instead of select.first() command in ATL whenever we are looking an object up with a unique attribute. For the ATL language, they implement a fixed any version as well to improve the performance more. However, the current paper employs the original version of any in ATL. We checked the Epsilon language and identified that any is shortcut as soon as an element validating condition is found. Therefore, it is a well-defined iterator in contrast to the select command in Return-First Command. More detail about any syntax in Epsilon can be found in [36].

  2. Boat Anchor is a known anti-pattern in traditional software development, which refers to a piece of software that serves no useful purpose in the current project [31].

  3. Architectural model transformation patterns address solutions to the organizing of transformations systems in order to enhance the modularity, verifiability and efficiency of these systems [13].

  4. https://www.eclipse.org/emf/compare/.

  5. http://www.transformation-tool-contest.eu/2014/solutions_fixml.html.

  6. https://git.eclipse.org/c/emf/org.eclipse.emf.git/tree/plugins/org.eclipse.emf.ecore/model/Ecore.ecore.

  7. ATLAS Transformation Language.

  8. Query/View/Transformation.

  9. https://www.eclipse.org/emf-refactor/.

  10. https://www.eclipse.org/epsilon/doc/epl/.

  11. https://www.eclipse.org/epsilon/doc/etl/.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Mahsa Panahandeh

  2. Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, Canada

    Mohammad Hamdaqa

  3. School of Computer Science, Reykjavik University, Reykjavík, Iceland

    Mohammad Hamdaqa

  4. MDSE Research Group, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Bahman Zamani

  5. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Abdelwahab Hamou-Lhadj

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  1. Mahsa Panahandeh
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  2. Mohammad Hamdaqa
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  3. Bahman Zamani
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  4. Abdelwahab Hamou-Lhadj
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Correspondence to Bahman Zamani.

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Communicated by Kevin Lano.

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Appendices

Appendix

A Supplementary images

See Figs. 21, 22 and 23.

B Scheduling anti-pattern and pattern solutions

The anti-pattern identification and pattern application in this paper were implemented using Epsilon language. Therefore, the execution schema for each one follows Epsilon execution semantics, which can be found for both EPLFootnote 10 and ETLFootnote 11 on the Epsilon website. In the following we elaborate on the scheduling rules of anti-patterns and pattern solutions that are used in this paper. Each scheduling rule is a task performed by Epsilon statements in form of pre, post, transformation rule, do-blocks, and/or a function call. Our implementation is based on the iterative mode of Epsilon execution semantics, in which the anti-pattern identification is repeated until no more matches have been found in the TDM elements.

Fig. 22
figure 22

Transformation high-level design meta-model of MeTAGeM [6]

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Fig. 23
figure 23

Transformation low-level design meta-model of MeTAGeM [6]

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The Spaghetti Transformation anti-pattern starts with defining a global variable to keep the status of mapping elements in the TDM. Then, a rule iteratively checks all the mapping elements of a TDM against the high-level design meta-models. Each mapping that does not satisfy the constraints shown in Table  1 is added to a set, called patternSet which keeps mappings with Spaghetti Transformation scenario. Also, the global value is changed to show the presence of Spaghetti Transformation scenario in the TDM, i.e, it is changed from false to true. Finally, after matching all the mappings, the result is printed for the developer. In Epsilon, the result of an EPL code, i.e., anti-pattern identification, can be stored and transformed between the other languages of the Epsilon family using a set, patternSet.

Phased Construction An ETL code is executed to apply Phased Construction solution to the original input TDM. First, the patternSet, provided by identifying spaghetti, including the mappings with Spaghetti scenario is taken as an input. To keep the original TDM, a new TDM is generated and  Phased Construction solution is applied to a new one. An initial rule generates independent elements on mapping concept in the TDM. Then, A rule recreates those mappings which are not a member of the patternSet as a copy of these elements in the original TDM. Moreover, features and dependencies of these mappings are set same as the original TDM. Then, each mapping in the patternSet, i.e., a mapping which needs to be restructured, is processed. A rule creates a new mapping element for each mapping in the patternSet while the created element has the same features as the original mapping, but it only accesses one level of the target meta-model. In fact, for each mapping in the patternSet, the number of involved target elements are counted using the conditional statement in Table 2, and for each one, a new mapping is generated. The rest of the features and dependencies of the created mapping element are populated using the original mapping. It is noticeable that this paper employs the top-down approach for restructuring spaghetti mappings. However, mappings in patternSet are processed in order that they have been defined by the developer in the TDM. In both transML and MeTAGeM, considering the mappings order in TDM is a developer’s task.

Frequent Invocation Regarding the TDM meta-model, each low-level TDM defines the transformation rule body inside of the Operation elements. Identifying the Frequent Invocation is performed by a rule which for each transformation rule, in the order that they have been defined in the TDM, checks whether its Operation body contains the select command. If so, it puts the Operation element in a set, patternSet. If the patternSet contains any Operation, then the developer is informed of the presence of Frequent Invocation.

Object Indexing Scheduling schema of applying Object Indexing on TDMs starts with a rule which regenerates elements of the original TDM, except the Operation elements. Operation elements are generated if those are not a member of the patternSet. Otherwise, identified Operations with Frequent Invocation scenario, are differently scheduled. For each Operation in the patternSet, a rule of the schema extracts the context of the select command, instances of the invoked context, and invoked property by the select command. Then, a map structure is generated by the invoked property value as the index associated with the invoked instances of the context type. This map is stored and to be used instead of the select command. Then, the schema rule restructures the TDM by changing the Operation body and substitutes the select command by the generated map structure. The changed Operation is added to the related element of the TDM and dependencies are set same as the original TDM. Finally, schema is repeated to check the next Operation in the patternSet.

Return-First Command The scheduling schema for identifying the Return-First Command is same as the scheduling for Frequent Invocation. However, in this scenario, the scheduling rule checks the body of all Operations against the select().first() command. Then, if Operations contain the checked command, the scheduling rule puts them in the patternSet.

Usage of Iterators Applying the Usage of Iterators is similar to the Object Indexing. A scheduling rule regenerates all the elements of the original TDM. However, Operation elements or transformation rule bodies are regenerated if they are not included in the patternSet. Otherwise, a rule is scheduled to find the conditional statement of the select command in the Operation body. Then, inside the Operation element, the present select(condition).first() command is substituted with the any(condition) statement. Finally, the changed Operation is added to the related transformation rule in the new TDM and dependencies are set same as the original TDM. The schema is repeated for all Operation elements in the patternSet.

Boat Anchor A rule collects all element types of the transformation source model and puts them in a set called Source. Then, a secondary rule iteratively checks each member of the Source set against the TDM elements. If the Source member is not a member of the TDM elements, it will be added to the ExcludedSet. Finally, another rule checks the ExcludedSet and informs the developer of the existing Boat Anchor if the set is not empty.

Filtering solution The ExcludedSet in identifying Boat Anchor is transmitted to the applying Filtering solution in patternSet. In this scenario, the transformation source model is restructured. A premier rule regenerates transformation source model elements except element types included in the patternSet, i.e., elements that need to be excluded from the transformation source model. Each element in patternSet, i.e., excluding element, is ignored by the premier rule if it does not have any dependent element. Otherwise, a secondary rule is called to remove the excluding element dependency in the model hierarchy. In fact, the secondary rule regenerates the excluding elements and its dependent element in a flat view and adds them to the generated model. Next, the execution schema is returned to the premier rule and it removes the excluding element. Then, the premier rule continues by checking and regenerating the rest of the transformation source model elements.

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Panahandeh, M., Hamdaqa, M., Zamani, B. et al. MUPPIT: a method for using proper patterns in model transformations. Softw Syst Model 20, 1491–1523 (2021). https://doi.org/10.1007/s10270-020-00853-y

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