A domain-specific language for describing machine learning datasets

Giner-Miguelez, Joan; Gómez, Abel; Cabot, Jordi

doi:10.1016/j.cola.2023.101209

Cited by 10 publications

(5 citation statements)

References 29 publications

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“…We also plan to work on a dedicated language for GNN Properties, more smoothly integrated with VPDL. To this end, we can study the potential reuse of existing work on DSLs for supporting ML activities [11,15].…”

Section: Discussionmentioning

confidence: 99%

Towards the Integration Support for Machine Learning of Inter-Model Relations in Model Views

Pontes Miranda,

Bruneliere,

Tisi

et al. 2024

Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing

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Model-driven engineering (MDE) supports the engineering of complex systems via multiple models representing various systems' aspects. These interrelated models are usually heterogeneous and often specified using complementary modeling languages. Whenever needed, model view solutions can be employed to federate these models in a more transparent way. To do so, the required inter-model links can sometimes be automatically computed via explicitly written matching rules. However, in some cases, matching rules would be too complex (or even impossible) to write. Thus, some inter-model links may be inferred by analyzing previous examples instead. In this paper, we introduce a Machine Learning (ML)backed approach for expressing and computing such model views. Notably, we aim at making the use of ML as simple as possible in this context. To this end, we propose to refine and extend the View-Point Definition Language (VPDL) from the EMF Views model view solution to integrate the use of dedicated Heterogeneous Graph Neural Networks (HGNNs). These view-specific HGNNs can be trained with appropriate sets of contributing models before being used for inferring links to be added to the views. CCS CONCEPTS• Software and its engineering → System modeling languages; Integration frameworks; • Computing methodologies → Neural networks.

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Section: Discussionmentioning

confidence: 99%

Towards the Integration Support for Machine Learning of Inter-Model Relations in Model Views

Pontes Miranda,

Bruneliere,

Tisi

et al. 2024

Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing

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“…There are many other tokens defined for operators, symbols, and keywords. Given the token definitions in Listing 5, the lexical analysis process produces the following sequence of tokens for the statement A = [1,2,3].…”

Section: Lexical Analysismentioning

confidence: 99%

“…Table 2 shows how to initialize a variable of type 'Matrix' in the MxPL language. A = [ [1,2,3,4], [5,6,7,8], [9,10,11,12], [13,14,15,16]];…”

Section: Program Examplesmentioning

confidence: 99%

“…Special-purpose languages especially serve to develop real-time applications that deliver superior operational performance during interpretation or execution. They can provide a more efficient and easier way to solve a certain class of problems [3][4][5][6][7] and can hide the runtime code to increase the system security. Some illustrative programming areas include document formatting [8][9][10], relational database query [11][12][13][14], symbolic computation [15][16][17][18], hardware description [19][20][21][22], and animation and modeling [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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MxPL: A Programming Language for Matrix-Related Operations

Aydoğdu,

Pehlivan

2024

Symmetry

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It is important to establish solid mathematical knowledge throughout the education period. Matrices are important mathematical objects commonly used in many diverse disciplines, including mathematics, engineering, and science. Most problems encountered in such disciplines are represented by mathematical models with various types of matrices, and solved through some applications of matrix algebra. Although simple or advanced operations of matrices can be performed by using modern programming languages, it usually results in a large fragment of code with a low level of readability due to a complicated sequence of control statements. On the other hand, special-purpose languages handle these operations via library functions, presenting poor integration with other programming environments, and less programming flexibility and practice. This paper addresses the design and development of a programming language, called MxPL, which supports matrix-related mathematics with the provision of some basic structures and functions. Firstly, a grammar that is compatible with the usual notations of matrices is constructed and the parser is produced. Then, the code verifier and interpreter for MxPL programs are implemented. Some code examples are presented to illustrate the performance of several sophisticated matrix operations. The comparative analysis of MxPL is conducted with modern programming languages based on language features.

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“…In this process, programmers can use prompt patterns [34], which are similar to software patterns, reusable prompts to solve common problems in LLM interaction. One such prompt pattern is the domain-specific language (DSL) [35,36] creation pattern.…”

Section: Introductionmentioning

confidence: 99%

Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers

Kosar,

Ostojić,

Liu

et al. 2024

Mathematics

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The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.

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A domain-specific language for describing machine learning datasets

Cited by 10 publications

References 29 publications

Towards the Integration Support for Machine Learning of Inter-Model Relations in Model Views

Towards the Integration Support for Machine Learning of Inter-Model Relations in Model Views

MxPL: A Programming Language for Matrix-Related Operations

Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers

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