Combining OCL and Natural Language: a Call for a Community Effort

The growing popularity and availability of pretrained natural language models opens the door to many interesting applications combining natural language (NL) with software artefacts. A couple of examples are the generation of code excerpts from NL instructions or the verbalization of programs in NL to facilitate their comprehension. Many of these language models have been trained with open source software datasets and therefore understand a variety of programming languages, but not OCL. We argue that OCL needs to jump into the machine learning bandwagon or it will risk losing its appeal as a constraint specification language. For that, the key first task is to create together an OCL corpus dataset amenable for natural language processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyectos nacionales LOCOSS (PID2020-114615RB-I00) e IPSCA (PID2021-125527NB-I00) Projectos de la Comisión Europea TRANSACT (grant agreement No 101007260) and AIDOaRt (grant agreement No 101007350

Model Transformation Testing and Debugging: A Survey

Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state of the art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This paper presents a survey on testing and debugging model transformations based on the analysis of \nPapers~papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorise the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.This work is partially supported by the European Commission (FEDER) and Junta de Andalucia under projects APOLO (US-1264651) and EKIPMENT-PLUS (P18-FR-2895), by the Spanish Government (FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación) under projects HORATIO (RTI2018-101204-B-C21), COSCA (PGC2018-094905-B-I00) and LOCOSS (PID2020-114615RB-I00), by the Austrian Science Fund (P 28519-N31, P 30525-N31), and by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development (CDG

A generic LSTM neural network architecture to infer heterogeneous model transformations.

Models capture relevant properties of systems. During the models’ life-cycle, they are subjected to manipulations with different goals such as managing software evolution, performing analysis, increasing developers’ productivity, and reducing human errors. Typically, these manipulation operations are implemented as model transformations. Examples of these transformations are (i) model-to-model transformations for model evolution, model refactoring, model merging, model migration, model refinement, etc., (ii) model-to-text transformations for code generation and (iii) text-to-model ones for reverse engineering. These operations are usually manually implemented, using general-purpose languages such as Java, or domain-specific languages (DSLs) such as ATL or Acceleo. Even when using such DSLs, transformations are still time-consuming and error-prone. We propose using the advances in artificial intelligence techniques to learn these manipulation operations on models and automate the process, freeing the developer from building specific pieces of code. In particular, our proposal is a generic neural network architecture suitable for heterogeneous model transformations. Our architecture comprises an encoder–decoder long short-term memory with an attention mechanism. It is fed with pairs of input–output examples and, once trained, given an input, automatically produces the expected output. We present the architecture and illustrate the feasibility and potential of our approach through its application in two main operations on models: model-to-model transformations and code generation. The results confirm that neural networks are able to faithfully learn how to perform these tasks as long as enough data are provided and no contradictory examples are given.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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 revo- lutionize 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.Funding for open access publishing: Universidad de Málaga/ CBU

Towards an Extensible Architecture and Tool Support for Model-based Verification.

Model-based software engineering (MBSE) brings models to the center of software and system design. Models are powerful abstractions used to support all phases of the software development life cycle of complex software. As these models grow larger and their complexity increases, they need to be verified and validated to preserve their correctness. One possible way to do so is by means of the use of formal methods. However, the availability of MBSE tools with support for validation and verification is limited, and they usually require the cumbersome deployment of software burdened by dependencies, preventing the adoption of these tools. This paper presents a web-based architecture designed to support the definition of domain models and provide translation capabilities to different verification formalisms. As a proof of concept for our architecture, we have developed a tool prototype that is light-weight, runs in the browser and supports: (i) definition of domain models represented as class diagrams and (ii) partial translation of class diagrams into the Alloy specification language, enabling verification of structural domain properties. We show how we have used this tool to verify properties for the public bus management system in the city of Málaga, Spain.This work was partially funded by Universidad de Málaga (Campus Internacional de Excelencia), and the Spanish Government under projects PID2021-125527NB-I00 and TED2021-130523B-I00. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Dealing with Belief Uncertainty in Domain Models.

There are numerous domains in which information systems need to deal with uncertain information. These uncertainties may originate from different reasons such as vagueness, imprecision, incompleteness or inconsistencies; and, in many cases, they cannot be neglected. In this paper, we are interested in representing and processing uncertain information in domain models, considering the stakeholders’ beliefs (opinions). We show how to associate beliefs to model elements, and how to propagate and operate with their associated uncertainty so that domain experts can individually reason about their models enriched with their personal opinions. In addition, we address the challenge of combining the opinions of different domain experts on the same model elements, with the goal to come up with informed collective decisions. We provide different strategies and a methodology to optimally merge individual opinions.This work is partially supported by Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the Spanish Government under projects LO-COSS (PID2020-114615RB-I00), CoSCA (PGC2018-094905-B-I00) and MBTI4A (P20-00067-FR); and TRANSACT, which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 101007260. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Netherlands, Finland, Germany, Poland, Austria, Spain, Belgium, Denmark, and Norway

Specializing Scope Graph Resolution Queries

To warrant programmer productivity, type checker results should be correct and available quickly. Correctness can be provided when a type checker implementation corresponds to a declarative type system specification. Statix is a type system specification language which achieves this by automatically deriving type checker implementations from declarative typing rules. A key feature of Statix is that it uses scope graphs for declarative specification of name resolution. However, compared to hand-written type checkers, type checkers derived from Statix specifications have sub-optimal run time performance.In this paper, we identify and resolve a performance bottleneck in the Statix solver, namely part of the name resolution algorithm, using partial evaluation. To this end, we introduce a tailored procedural intermediate query resolution language, and provide a specializer that translates declarative queries to this language.Evaluating this specializer by comparing type checking run time performance on three benchmarks (Apache Commons CSV, IO, and Lang3), shows that our specializer improves query resolution time up to 7.7x, which reduces the total type checking run time by 38 - 48%.<br/

Software assistants in software engineering: A systematic mapping study

International audienceThe increasing essential complexity of software systems makes current software engineering methods and practices fall short in many occasions. Software assistants have the ability to help humans achieve a variety of tasks, including the development of software. Such assistants, which show human-like competences such as autonomy and intelligence, help software engineers do their job by empowering them with new knowledge. This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user-assistant interactions. To this end, we followed the standard systematic mapping study method to identify and classify relevant works in the state of the art. Out of the 7580 articles resulting from the automatic search, we identified 112 primary studies that present works which qualify as software assistants. We provide all the resources needed to reproduce our study. We report on the trends and goals of the assistants, the tasks they perform, how they interact with users, the technologies and mechanisms they exploit to embed intelligence and provide knowledge, and their level of automation. We propose a classification of software assistants based on interactions and present an analysis of the different automation patterns. As outcomes of our study, we provide a classification of software assistants dealing with the design, construction and maintenance phases of software development, we discuss the results, identify open lines of work and challenges and call for new innovative and rigorous research efforts in this field

Polyglot Software Development: Wait, What?

International audienceThe notion of polyglot software development refers to the fact that most software projects nowadays rely on multiple languages to deal with widely different concerns, from core business concerns to user interface, security, and deployment concerns among many others. Many different wordings around this notion have been proposed in the literature, with little understanding of their differences. In this article, we propose a concise and unambiguous definition of polyglot software development including a conceptual model and its illustration on a well-known, open-source project. We further characterize the techniques used for the specification and operationalization of polyglot software development with a feature model, concentrating on polyglot programming. We conclude the article outlining the many challenges and perspectives raised by polyglot software development