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         "pages": "1141\u20131145","abstract": "Domain-specific Languages (DSLs) assist in building complex software systems across various domains by creating models that are interpreted or transformed into executable code. Language developers face a tedious and error-prone process when transforming such models directly into multiple target General-Purpose Programming Languages (GPLs) like Java and C++, requiring them to write DSL-specific transformations redundantly multiple times. Using intermediate languages can reduce this redundancy by enabling language developers to write DSL-specific transformation logic only once. Despite its potential, no approach currently uses the Action Language for Foundational UML (ALF) as an intermediate language to transform tailored DSLs within an industry-strength workbench for designing projectional languages. To address this, we develop ALFI as a prototypical implementation of ALF in Jet-Brains MPS and implement MPS transformations from ALFI to Java and C++. We develop two exemplary DSLs to demonstrate the transformation chains that transform tailored DSLs into ALFI and then into executable Java and C++ code. The prototypical ALFI implementation successfully demonstrates the use of ALF as an intermediate language, enabling the model transformation of DSLs into multiple target GPLs.",
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         "pages": "163--181","abstract": "Many enterprise software systems provide complex Graphical User Interfaces (GUIs) that need robust architectural patterns for well-structured software design. However, popular GUI architectural patterns like Model-View-ViewModel (MVVM) often lack detailed implementation guidance, leading GUI developers to inappropriately use the pattern without a comprehensive overview of design variants and often-mentioned trade-offs. Therefore, this paper presents an extensive review of MVVM design aspects and trade-offs, extending beyond the standard MVVM definition. We conducted a multivocal literature review (MLR), including white and gray literature, to cover essential knowledge from blogs, published papers, and other unpublished formats like books. Using the standard MVVM definition as a baseline, our study identifies (1) 76 additional design constructs grouped into 29 design aspects and (2) 16 additional benefits and 15 additional drawbacks. These insights can guide enterprise application developers in implementing practical MVVM solutions and enable informed design decisions.",
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         "author": [ 
            "Floriment Klinaku","Sarah Sophie Stieß","Steffen Becker"
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            	{"first" : "Sarah Sophie",	"last" : "Stieß"},
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         "author": [ 
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            	{"first" : "Floriment",	"last" : "Klinaku"},
            	{"first" : "Sarah Sophie",	"last" : "Stieß"},
            	{"first" : "Alireza",	"last" : "Hakamian"},
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         "editor": [ 
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         "volume": "228","pages": "112432","abstract": "The cloud computing model enables the on-demand provisioning of computing resources, reducing manual management, increasing efficiency, and improving environmental impact. Software architects now play a strategic role in designing and deploying elasticity policies for automated resource management. However, creating policies that meet performance and cost objectives is complex. Existing approaches, often relying on formal models like Queueing Theory, require advanced skills and lack specific methods for representing elasticity within architectural models. This paper introduces an architectural view type for modeling and simulating elasticity, supported by the Scaling Policy Definition (SPD) modeling language, a visual notation, and precise simulation semantics. The view type is integrated into the Palladio ecosystem, providing both conceptual and tool-based support. We evaluate the approach through two single-case experiments and a user study. In the first experiment, simulations of elasticity policies demonstrate sufficient accuracy when compared to load tests, showing the utility of simulations for evaluating elasticity. The second experiment confirms feasibility for larger applications, though with increased simulation times. The user study shows that participants completed 90% of tasks, rated the usability at 71%, and achieved an average score of 76% in nearly half the allocated time. However, the empirical evidence suggests that modeling with this architectural view requires more time than modeling control flow, resource environments, or usage profiles, despite its benefits for elasticity policy design and evaluation.",
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         "label" : "LLM-Based Explainability at Design Time: Detecting Elasticity Antipatterns in Software Architectures",
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         "booktitle": "Software Architecture. ECSA 2025 Tracks and Workshops","publisher":"Springer Nature Switzerland","address":"Cham",
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         "author": [ 
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            	{"first" : "Jonas",	"last" : "Lammert"},
            	{"first" : "Steffen",	"last" : "Becker"}
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         "editor": [ 
            "Domenico Bianculli","Hassan Sartaj","Vasilios Andrikopoulos","Cesare Pautasso","Tommi Mikkonen","Jennifer Perez","Tomás Bures","Martina De Sanctis","Henry Muccini","Elena Navarro","Mohamed Soliman","Uwe Zdun"
         ],
         "editors": [
         	
            	{"first" : "Domenico",	"last" : "Bianculli"},
            	{"first" : "Hassan",	"last" : "Sartaj"},
            	{"first" : "Vasilios",	"last" : "Andrikopoulos"},
            	{"first" : "Cesare",	"last" : "Pautasso"},
            	{"first" : "Tommi",	"last" : "Mikkonen"},
            	{"first" : "Jennifer",	"last" : "Perez"},
            	{"first" : "Tomás",	"last" : "Bures"},
            	{"first" : "Martina",	"last" : "De Sanctis"},
            	{"first" : "Henry",	"last" : "Muccini"},
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            	{"first" : "Mohamed",	"last" : "Soliman"},
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         "pages": "141--154","abstract": "As software architecture grows in complexity, understanding the implications of design decisions becomes increasingly challenging. Large Language Models (LLMs) offer new opportunities for enhancing explainability during architecture modeling and evaluation by generating natural language explanations that support comprehension, learning, and decision-making. This potential is particularly valuable in domains with increased technical complexity---such as elasticity in cloud-based systems. In this work, we integrate and evaluate LLM-based explanations in supporting design-time evaluation of software architectures, focusing on the detection of elasticity antipatterns. Elasticity antipatterns are flawed autoscaling policy configurations that potentially lead to inefficient or unreliable system behavior. We extend an existing modeling and simulation approach with a novel feature that generates contextualized, textual explanations derived from simulation data. These explanations aim to guide architects in understanding scaling behaviors, identifying design issues, and refining their models. Our contribution includes the conceptualization of explanation types relevant to elasticity modeling, the design of prompt templates to elicit effective responses from LLMs, and an evaluation of the generated explanations' usefulness and quality. Results indicate that LLM-assisted feedback enhances the interpretability of elasticity models and supports the early identification of antipatterns, albeit with some limitations in precision and conciseness with only a slight agreement between expert evaluations (\\$\\$\\backslashkappa \\$\\$$\\kappa$ = 0.202). The explanation quality across types of explanations differs. Even though most explanations contain factual information, a large portion was deemed as imprecise especially in explaining problem and solution, the policy and target and service level objectives.",
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