1,721,007 research outputs found
Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models
No full textContext: Due to the proliferation of generative AI models in different software engineering tasks, the research community has started to exploit those models, spanning from requirement specification to code development. Model-Driven Engineering (MDE) is a paradigm that leverages software models as primary artifacts to automate tasks. In this respect, modelers have started to investigate the interplay between traditional MDE practices and Large Language Models (LLMs) to push automation. Although powerful, LLMs exhibit limitations that undermine the quality of generated modeling artifacts, e.g., hallucination or incorrect formatting. Recording modeling operations relies on human-based activities to train modeling assistants, helping modelers in their daily tasks. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., security or privacy issues. Objective: In this paper, we propose an extension of a conceptual MDE framework, called MASTER-LLM, that combines different MDE tools and paradigms to support industrial and academic practitioners. Method: MASTER-LLM comprises a modeling environment that acts as the active context in which a dedicated component records modeling operations. Then, model completion is enabled by the modeling assistant trained on past operations. Different LLMs are used to generate a new dataset of modeling events to speed up recording and data collection. Results: To evaluate the feasibility of MASTER-LLM in practice, we experiment with two modeling environments, i.e., CAEX and HEPSYCODE, employed in industrial use cases within European projects. We investigate how the examined LLMs can generate realistic modeling operations in different domains. Conclusion: We show that synthetic traces can be effectively used when the application domain is less complex, while complex scenarios require human-based operations or a mixed approach according to data availability. However, generative AI models must be assessed using proper methodologies to avoid security issues in industrial domains
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multidimensional Context Modeling Applied to Non-Functional Analysis of Software
Full text linkContext awareness is a first-class attribute of today software systems. Indeed, many applications need to be aware of their context in order to adapt their structure and behavior for offering the best quality of service even in case the software and hardware resources are limited. Modeling the context, its evolution, and its influence on the services provided by (possibly resource constrained) applications are becoming primary activities throughout the whole software life cycle, although it is still difficult to capture the multidimensional nature of context. We propose a framework for modeling and reasoning on the context and its evolution along multiple dimensions. Our approach enables (1) the representation of dependencies among heterogeneous context attributes through a formally defined semantics for attribute composition and (2) the stochastic analysis of context evolution. As a result, context can be part of a model-based software development process, and multidimensional context analysis can be used for different purposes, such as non-functional analysis. We demonstrate how certain types of analysis, not feasible with context-agnostic approaches, are enabled in our framework by explicitly representing the interplay between context evolution and non-functional attributes. Such analyses allow the identification of critical aspects or design errors that may not emerge without jointly taking into account multiple context attributes. The framework is shown at work on a case study in the eHealth domain
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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