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In this paper we describe textX, a meta-language and a tool for building Domain-Specific Languages. It is implemented in Python using Arpeggio PEG (Parsing Expression Grammar) parser library. From a single language description (grammar) textX will build a parser and a meta-model (a.k.a. abstract syntax) of the language. The parser is used to parse...
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... Unittest Implementation: The TestAST class is a key part of any testing framework, used to check over important code evaluation parts like EvalCode and EvalVisit that analyze programs on the fly [24]. This paper looks closer at how those components evaluate dynamic code. ...
Analyzing and evaluating student-generated code poses a big headache for programming education. Code evaluation is a delicate task requiring accuracy, efficiency, and error-checking. This paper considers one of the many useful tools in code evaluation. It's Abstract Syntax Trees (AST). AST is a robust tool for reading, filtering, and weighing student code. It is built from tree regular expressions for common programming patterns. This paper considers, firstly, integrating AST into code evaluation projects. Secondly and most importantly to the present implementation work is that Python programming language provides excellent compatibility for implementing such a program. Taking advantage of Python's feature that allows the inputting of code as a string, AST records and tracks everything about the user's own source. With AST being integrated into platforms for education, how to evaluate student code has been completely altered; as sites such as LeetCode and HackerRank testify. This paper presents an accurate, efficient and error-aware approach to code evaluation by improving on educational websites incorporating AST. Adapting well to the dynamics of programming education, this elaborate assessment system will provide students with truthful assessments and profound feedback on their coding capabilities. This article shows how AST-based evaluation has revolutionized the way programming is taught, and thoroughly explores its role in evaluating code.
Adolescents’ emotional changes will have a huge impact on themselves; perhaps, they do not understand themselves. However, according to research, many behaviors of adolescents are often accompanied by emotional changes, and the occurrence of these changes will also bring about their unconsciousness. This article first introduces the research background, significance, and development status of smart home sensors and young people’s emotions at home and abroad. This article then gives a detailed introduction to the Python language, intelligent sensor networks, and real-time analysis of youth emotions. In the introduction, it mainly explains the design of the intelligent sensor network system and introduces the system architecture and software and hardware design of the wireless sensor network in detail. In the hardware part, it mainly gives a brief overview of information collection, data transmission, and data processing. In the software part, the embedded software design of three types of network nodes and the control center software design based on Python are given. Finally, the neural network algorithm is used to realize the real-time analysis of young people’s emotions, and the recognition rate of multiple algorithms and the data situation of multiple emotional factors are tested at the same time. The results show that the highest recognition rate of 58.4% can be achieved on the validation set of the HAPPEI database after preprocessing, which is higher than the recognition result obtained by directly training the network using the training set of the HAPPEI database.
In model-driven engineering, a Domain-Specific Language (DSL) is a specialized language, which, combined to a transformation function, serves to raise the abstraction level of software and ease software development. However, in practice, beyond this general definition, DSLs adopt multiple forms of representation and implementation. Actually, the issue is projects, and mainly large-scale projects, have to deal with DSL and DSL tool variants. The purpose of this paper is to propose a DSL feature model in order to identify DSL and DSL tool variability.