An algorithm for generating abstract syntax trees

Noonan, Robert E.

doi:10.1016/0096-0551(85)90018-9

Cited by 30 publications

(13 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…<EDD>::=<Header>{<Element List>} <Header>::=<MANUFACTURER><DEVICE_TYPE> <DEVICE_REVISION><DD_REVISION > <Element List >::= <Menu>|<Variable>|<Method>|<Command>|<Relation> <Command>::=<Command header><Command body> <Command header >::=Command<ID> <Command body>::=<Command_attribute_list> <Command_attribute_list>::= <Command_number>|<Command_operation>|<Comma nd_transaction_list>| [Command_respons_code] <Command_number_attribute>::=Number <integer>; <Command_operation_attribute>::=<Enum_operation> <Command_transaction_list>::= <Command_request>|<Command_reply> <Command_request>::=<Variable List> <Command_reply>::=<Variable List> <Command_respons_code>::=<Enum_respons_code> <Variable List>::=ID|<Variable List >,ID 2) AST (Abstract Syntax Tree) AST is an intermediate procedure representation which is obtained from syntax and lexical analysis [6], reflecting the syntax structure of the source code, whose tree nodes is used to represent the syntax element and corresponds with different layers of the syntax structure of language respectively. By searching for the syntax tree syntax structure of the source code can be analyzed in-depth and handled accordingly.…”

Section: ) Syntax Representationmentioning

confidence: 99%

Research on Fieldbus Device Integration Based on EDD and FDT

Shan

Wang²,

Li³

et al. 2010

2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics

View full text Add to dashboard Cite

Aimed to the needs for interoperability and comprehensive integration on fieldbus, On the basis of introducing the characteristics of EDD ( Electronic Device Description)and FDT (Field Device Too1), an integration model based on EDD and FDT is proposed. Through embedding the EDDL (Electronic Device Description Language) interpreter into the device DTM and adding the user interface generator module in the DTM UI (User Interface), when the interpreter parse an EDD file, in terms of contents of the device description the intermediate data structure is generated, and by interacting with the viewbuilder user interfaces are automatically generated, and then the operation for the device with EDD can be implemented. As a consequence, the FDT frame application support both DTM (Device Type Manager) and EDD. Inner logic of the model is clarified, and the implementation of interpreter and user interface is specified in detail. The DTM based on EDD can be easily integrated into existing FDT frame application, attaining a purpose of flexible and wide device integration.

show abstract

Section: ) Syntax Representationmentioning

confidence: 99%

Research on Fieldbus Device Integration Based on EDD and FDT

Shan

Wang²,

Li³

et al. 2010

2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics

View full text Add to dashboard Cite

show abstract

“…The logical forms normally have equivalent structured representations to precisely express the complex relations between a set of objects. For instance, the executable codes written in Python or SQL could be parsed into abstract syntax tree(AST) (Noonan, 1985) denoting the mutual relations among occurred constructs in the source code, while the knowledge bases may be converted into knowledge graphs that depict the relations between entities with directed edges. Compared to the plain text inputs, the structure-aware encoding capturing not only the sequential information from texts but also the internal logic from structural representations recently has been proved to be more effective in several Graph-to-Text tasks (Song et al, 2018;Ribeiro et al, 2020).…”

Section: Structure-aware Encodingmentioning

confidence: 99%

Logic-Consistency Text Generation from Semantic Parses

Shu¹,

Zhang²,

Dong³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

View full text Add to dashboard Cite

Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries. This is challenging due to two reasons: (1) the complex and intensive inner logic with the data scarcity constraint, (2) the lack of automatic evaluation metrics for logic consistency. To address these two challenges, this paper first proposes SNOWBALL, a framework for logic consistent text generation from semantic parses that employs an iterative training procedure by recursively augmenting the training set with quality control. Second, we propose a novel automatic metric, BLEC, for evaluating the logical consistency between the semantic parses and generated texts. The experimental results on two benchmark datasets, Logic2Text and Spider, demonstrate the SNOWBALL framework enhances the logic consistency on both BLEC and human evaluation. Furthermore, our statistical analysis reveals that BLEC is more logically consistent with human evaluation than general-purpose automatic metrics including BLEU, ROUGE and, BLEURT. Our data and code are available at https://github.com/ Ciaranshu/relogic.

show abstract

“…In parse trees, the roots of subtrees represent nonterminal symbols of the grammar, while leaves represent terminal grammar symbols. In an abstract syntax tree, operators represent root nodes, while leaves symbolize operands [60]. Once the trees are generated, tree-matching algorithms are used to find the similar code fragments.…”

Section: Code Clone Detection: Techniques and Toolsmentioning

confidence: 99%

What is Your Code Clone Detection and Evolution Research Made Of?

Wijesiriwardana

Wimalaratne

2021

cai

View full text Add to dashboard Cite

Over the past few decades, clone detection and evolution have become a major area of study in software engineering. Clone detection experiments present several challenges to researchers such as accurate data collection, selecting proper code detection algorithms, and understanding clone evolution phenomena. This paper attempts to facilitate clone detection and evolution research by providing a structured and systematic mechanism to conduct experiments. Clone detection experiments usually consist of several tasks such as fetching data from a version control system, performing necessary pre-processing activities, and feeding the data to a clone detection algorithm. Therefore, a particular clone detection experiment can interpret as a meaningful combination of such tasks into a scientific workflow. In this work, the concrete tasks in a code clone detection workflow are referred to as Building Blocks. This paper presents a useful collection of Building Blocks identified based on a systematic literature review, and a conceptual framework of an experimental testbed to facilitate clone detection experiments. The reusability of the Building Blocks was validated using four case studies selected from the literature. The validation results confirm the reusability and the expressiveness of the Building Blocks in new ventures. Besides, the proposed experimental testbed is proven beneficial in conducting and replicating clone detection experiments.

show abstract

An algorithm for generating abstract syntax trees

Cited by 30 publications

References 3 publications

Research on Fieldbus Device Integration Based on EDD and FDT

Research on Fieldbus Device Integration Based on EDD and FDT

Logic-Consistency Text Generation from Semantic Parses

What is Your Code Clone Detection and Evolution Research Made Of?

Contact Info

Product

Resources

About