Claimed advantages and disadvantages of (dedicated) model transformation languages: a systematic literature review

Götz, Stefan; Tichy, Matthias; Groner, Raffaela

doi:10.1007/s10270-020-00815-4

Cited by 10 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, however, such claims have two main flaws. First, as discussed by Götz et al, there is a lack of actual evidence to have confidence in their genuineness [2]. Second, we argue that most of these claims emerged together with the first MTLs around 15 years ago.…”

Section: Context and Motivationmentioning

confidence: 55%

“…In the literature, many advantages are ascribed to model transformation languages, such as better analysability, comprehensibility or expressiveness [2]. Moreover, model transformation languages aim at abstracting from certain recurring aspects of a model transformation such as traversing the input model or creating and managing trace information, claiming to hide complex semantics behind a simple and intuitive syntax [1,[3][4][5].…”

Section: Context and Motivationmentioning

confidence: 99%

“…In principle, a shift towards more data-driven development of transformations is therefore possible. Whether this brings an overall advantage or not is still a debated topic [2] and in our eyes depends on the experience and preference of the developers. However, there are many studies in the field of object-oriented programming that establish a connection between cyclomatic complexity and reliability [36][37][38][39][40], i.e.…”

Section: Language Advancements and Their Influence On The Ability To Write Transformations: A Historical Perspectivementioning

confidence: 99%

See 2 more Smart Citations

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

2021

Self Cite

View full text Add to dashboard Cite

Model transformations are among the key concepts of model-driven engineering (MDE), and dedicated model transformation languages (MTLs) emerged with the popularity of the MDE pssaradigm about 15 to 20 years ago. MTLs claim to increase the ease of development of model transformations by abstracting from recurring transformation aspects and hiding complex semantics behind a simple and intuitive syntax. Nonetheless, MTLs are rarely adopted in practice, there is still no empirical evidence for the claim of easier development, and the argument of abstraction deserves a fresh look in the light of modern general purpose languages (GPLs) which have undergone a significant evolution in the last two decades. In this paper, we report about a study in which we compare the complexity and size of model transformations written in three different languages, namely (i) the Atlas Transformation Language (ATL), (ii) Java SE5 (2004–2009), and (iii) Java SE14 (2020); the Java transformations are derived from an ATL specification using a translation schema we developed for our study. In a nutshell, we found that some of the new features in Java SE14 compared to Java SE5 help to significantly reduce the complexity of transformations written in Java by as much as 45%. At the same time, however, the relative amount of complexity that stems from aspects that ATL can hide from the developer, which is about 40% of the total complexity, stays about the same. Furthermore we discovered that while transformation code in Java SE14 requires up to 25% less lines of code, the number of words written in both versions stays about the same. And while the written number of words stays about the same their distribution throughout the code changes significantly. Based on these results, we discuss the concrete advancements in newer Java versions. We also discuss to which extent new language advancements justify writing transformations in a general purpose language rather than a dedicated transformation language. We further indicate potential avenues for future research on the comparison of MTLs and GPLs in a model transformation context.

show abstract

Section: Context and Motivationmentioning

confidence: 55%

Section: Context and Motivationmentioning

confidence: 99%

Section: Language Advancements and Their Influence On The Ability To Write Transformations: A Historical Perspectivementioning

confidence: 99%

See 1 more Smart Citation

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unfortunately, empirical research on usage, advantages, and disadvantages of model transformation languages and corresponding tools is relatively scarce (Götz et al 2020) as works similar to the empirical studies by Hutchinson et al (2011) and Liebel et al (2018) on model-based engineering in general are still missing. Such empirical studies could influence the research in the area of model transformations and may, consequently, positively affect the adoption of model transformations in industrial practice (Burgueño et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Such empirical studies could influence the research in the area of model transformations and may, consequently, positively affect the adoption of model transformations in industrial practice (Burgueño et al 2019). Götz et al (2020) have identified 15 different categories of advantages and disadvantages of model transformations in their recent Systematic Literature Review (SLR). One of these 15 categories is the execution performance of model transformation.…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Relevance of the Performance of Model Transformations.

Groner¹,

Juhnke²,

Götz³

et al. 2021

JOT

Self Cite

View full text Add to dashboard Cite

In Model-Driven Engineering (MDE) model transformation languages are used to describe important operations on models.Such domain-specific languages are specially developed to describe transformation rules, according to which an output model should be generated from an input model. In comparison to these domain-specific languages, techniques to analyze and improve the performance of programs written in a general-purpose language, such as Java or C, are well known. However, are such techniques also needed for model transformation languages? Problem. Since these languages are only used in certain domains; the first question is whether performance is at all relevant for model transformations and whether techniques similar to those used to analyze and improve the performance of general-purpose languages are needed. Research in the performance of model transformations focuses mainly on comparing the performance of different languages or different definition styles or optimizing the engine that executes the transformation. However, it is not clear to what extent these efforts can mitigate or prevent performance issues, and there is also a lack of studies that examine to what extend the performance of transformations is relevant. Method. In order to close this gap and to answer the initial question about the relevance of performance, we conducted an online survey. For this purpose, we developed a questionnaire and identified 649 authors as potential participants based on a Systematic Literature Review (SLR) on a selection of model transformation languages. Additionally, we were able to acquire four further potential participants by advertising our study. In total, 84 participants took part in our survey. We used statistical tests such as Kendall's τ c , the Kruskal-Wallis-Test and the Mann-Whitney-U-Test to evaluate our hypotheses on relevant factors for the performance of model transformations. Results. The results show that specific performance is desired and that there is a willingness to improve performance. In this regard, we identified a need for insights necessary to better understand how a transformation is performed in order to be able to improve its performance. Furthermore, we investigated with the help of hypotheses tests the possible influencing factors that cause participants to try to analyze or improve the performance of model transformations. The main results of the hypotheses tests are that the satisfaction with the execution time, the size of the models used, the relevance of whether a specific execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation are relevant factors.

show abstract

Human factors in model-driven engineering: future research goals and initiatives for MDE

Liebel,

Klünder,

Hebig

et al. 2024

Softw Syst Model

View full text Add to dashboard Cite

Claimed advantages and disadvantages of (dedicated) model transformation languages: a systematic literature review

Cited by 10 publications

References 57 publications

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

A Survey on the Relevance of the Performance of Model Transformations.

Human factors in model-driven engineering: future research goals and initiatives for MDE

Contact Info

Product

Resources

About