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Models to code : with no mysterious gaps
Learn how to translate an executable model of your application into running code. This is not a book about theory, good intentions or possible future developments. You'll benefit from translation technology and solid software engineering principles that are demonstrated with concrete examples using an open source tool chain. Models don't deliver enough value if they are not on a direct path to code production. But to waste time building models that are merely pictures of your code doesn't add much value either. In this book, you'll translate detailed, yet platform-independent models that solve real application problems. Using a pragmatic approach, Models to Code quickly dives into two case studies of Executable UML models. The models and code are extensively annotated and illustrate key principles that are emphasized throughout the book. You'll work with code production using \"C\" as the implementation language and targeting microcomputer class processors. This might not be your particular target language or platform, but you can use you can use what you learn here to engineer or re-evaluate your own code translation system to dramatically increase the value of both your modeling and code generation solution. Written by three leading experts, Models to Code is an exceptional resource for producing software by model translation-- add it to your library today.
Book
On the assessment of generative AI in modeling tasks: an experience report with ChatGPT and UML
Most experts agree that large language models (LLMs), such as those used by Copilot and ChatGPT, are expected to revolutionize the way in which software is developed. Many papers are currently devoted to analyzing the potential advantages and limitations of these generative AI models for writing code. However, the analysis of the current state of LLMs with respect to software modeling has received little attention. In this paper, we investigate the current capabilities of ChatGPT to perform modeling tasks and to assist modelers, while also trying to identify its main shortcomings. Our findings show that, in contrast to code generation, the performance of the current version of ChatGPT for software modeling is limited, with various syntactic and semantic deficiencies, lack of consistency in responses and scalability issues. We also outline our views on how we perceive the role that LLMs can play in the software modeling discipline in the short term, and how the modeling community can help to improve the current capabilities of ChatGPT and the coming LLMs for software modeling.
Journal Article
Uncertainty representation in software models: a survey
This paper provides a comprehensive overview and analysis of research work on how uncertainty is currently represented in software models. The survey presents the definitions and current research status of different proposals for addressing uncertainty modeling and introduces a classification framework that allows to compare and classify existing proposals, analyze their current status and identify new trends. In addition, we discuss possible future research directions, opportunities and challenges.
Journal Article
Execution of UML models: a systematic review of research and practice
Several research efforts from different areas have focused on the execution of UML models, resulting in a diverse and complex scientific body of knowledge. With this work, we aim at identifying, classifying, and evaluating existing solutions for the execution of UML models. We conducted a systematic review in which we selected 63 research studies and 19 tools among over 5400 entries by applying a systematic search and selection process. We defined a classification framework for characterizing solutions for UML model execution, and we applied it to the 82 selected entries. Finally, we analyzed and discussed the obtained data. From the analyzed data, we drew the following conclusions: (i) There is a growing scientific interest on UML model execution; (ii) solutions providing translational execution clearly outnumber interpretive solutions; (iii) model-level debugging is supported in very few cases; (iv) only a few research studies provide evidence of industrial use, with very limited empirical evaluations; (v) the most common limitation deals with coverage of the UML language. Based on these observations, we discuss potential research challenges and implications for the future of UML model execution. Our results provide a concise overview of states of the art and practice for UML model execution intended for use by both researchers and practitioners.
Journal Article
Design of classical-quantum systems with UML
Developers of the many promising quantum computing applications that currently exist are urging companies in many different sectors seriously consider integrating this new technology into their business. For these applications to function, not only are quantum computers required, but quantum software also. Accordingly, quantum software engineering has become an important research field, in that it attempts to apply or adapt existing methods and techniques (or propose new ones) for the analysis, design, coding, and testing of quantum software, as well as playing a key role in ensuring quality in large-scale productions. The design of quantum software nevertheless poses two main challenges: the modelling of software quantum elements must be done in high-level modelling languages; and the need to further develop so-called “hybrid information systems”, which combine quantum and classical software. To address these challenges, we first propose a quantum UML profile for analysing and designing hybrid information systems; we then demonstrate its applicability through various structural and behavioural diagrams such as use case, class, sequence, activity, and deployment. In comparison to certain other quantum domain-specific languages, this UML profile ensures compliance with a well-known international standard that is supported by many tools and is followed by an extensive community.
Journal Article
Software engineering whispers: The effect of textual vs. graphical software design descriptions on software design communication
ContextSoftware engineering is a social and collaborative activity. Communicating and sharing knowledge between software developers requires much effort. Hence, the quality of communication plays an important role in influencing project success. To better understand the effect of communication on project success, more in-depth empirical studies investigating this phenomenon are needed.ObjectiveWe investigate the effect of using a graphical versus textual design description on co-located software design communication.MethodTherefore, we conducted a family of experiments involving a mix of 240 software engineering students from four universities. We examined how different design representations (i.e., graphical vs. textual) affect the ability to Explain, Understand, Recall, and Actively Communicate knowledge.ResultsWe found that the graphical design description is better than the textual in promoting Active Discussion between developers and improving the Recall of design details. Furthermore, compared to its unaltered version, a well-organized and motivated textual design description–that is used for the same amount of time–enhances the recall of design details and increases the amount of active discussions at the cost of reducing the perceived quality of explaining.
Journal Article
Practitioners’ experiences with model-driven engineering: a meta-review
The Object Management Group introduced the Model-Driven Architecture in 2001. Since then, the research community has embraced model-driven engineering (MDE), but to a lesser extent than practitioners had hoped. A good awareness of practitioners’ challenges, particularly with modeling, is required to ensure the relevance of a research agenda. Therefore, this study conducts a meta-review on the state of practice in using modeling languages for software engineering over the last five years using Kitchenham’s guidelines. This study serves as an orientation within the research field and a basis for further research. It contributes to the literature by focusing on publications discussing the practical use of modeling languages and the benefits and problems perceived by practitioners. The main finding of this review is that practitioners benefit from MDE in the following ways: it is beneficial for several stakeholders; it saves cost; it is easy to use; it improves productivity, quality, and understanding of the system; and it provides support for software development activities. However, practitioners continue to face several serious challenges. The most frequently reported issues are the missing tool functionalities. Many studies have found that adhering to the Physics of Notation principles would improve modeling languages. Other findings include that modeling is mostly used for documentation and requirements elicitation, and UML is the most often used.
Journal Article
Cancer hallmark analysis using semantic classification with enhanced topic modelling on biomedical literature
The ever-growing 1.15 million new cases of cancer on a yearly basis alone in India is a major cause of concern for the experts and researchers working in various biomedical organizations. The advent of modern text engineering strategies and NLP techniques can play a crucial role in the discovery and analysis of pre-existing knowledge present in the cancer related biomedical archives. The available 10 Cancer hallmarks can provide key insights and make significant impact in the ongoing cancer research. It is extremely important to identify and classify required information due to time and resource crunch which needs to be quickly accessed. This article introduces a novel machine learning framework called Cancer Hallmark Classification and Topic Modeling (CHCTM), designed for supervised learning. The CHCTM framework is capable of semantically learning, categorizing, and extracting significant topics and their combinations related to the hallmarks of cancer (HoC) from a dataset comprising 1499 PubMed documents. The key contributions of this research include the creation of an innovative ensemble classification model using a meta-classifier based on Random Forest (RF). Additionally, it introduces an Enhanced Latent Dirichlet Allocation (ELDA) topic modeling strategy to generate relevant mixtures of topics. The performance of the CHCTM framework is evaluated using precision, recall, accuracy, and F-score parameters. Comparative analysis with other biomedical baseline methods reveals an 8% improvement in F-score. The coherence values acquired for ELDA are tallied and weighted against PLSA and LDA models to demonstrate the effectiveness of this approach.
Journal Article