Formal Semantics Extraction from Natural Language Specifications for ARM

Vu, Anh V.; Ogawa, Mizuhito

doi:10.1007/978-3-030-30942-8_28

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…These authors [13] proposed a method for transforming specifications into source code. Their approach systematically extracts the formal semantics of the ARM instructions from their natural language specifications.…”

Section: Relate Workmentioning

confidence: 99%

Knowledge Extraction from the Language Extended Lexicon Glossary Using Natural Language Processing

Antonelli,

Lezoche,

Delle Ville

2024

TecnoL.

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Successful software development requires good specifications, that is, correct, consistent, and unambiguous requirements. Natural language artifacts are the most used tools for writing specifications; nevertheless, the use of natural language can introduce many defects such as ambiguity, vagueness, and generality. In light of this, the purpose of this paper is to propose an approach for writing good specifications and extracting knowledge from them. The proposed approach uses a particular lexicon, the Language Extended Lexicon (LEL) glossary, and suggests rules for extracting knowledge (concepts, attributes, and relationships) from it. This paper also presents a prototype that implements and supports the proposed approach, as well as a preliminary evaluation of the approach. In conclusion, the proposed approach represents a theoretical contribution, which was evaluated using the System Usability Scale, and its evaluation results are promising, as it obtained the maximum score.

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Section: Relate Workmentioning

confidence: 99%

Knowledge Extraction from the Language Extended Lexicon Glossary Using Natural Language Processing

Antonelli,

Lezoche,

Delle Ville

2024

TecnoL.

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“…In addition to N implementations, our approach tests the specification as well using a mechanized specification. Recently, several approaches to extract syntax and semantics directly from language specifications are presented [11]- [13]. We utilize them to bridge the gap between specifications and their implementations through conformance tests generated from mechanized specifications.…”

Section: Introductionmentioning

confidence: 99%

JEST: N+1-version Differential Testing of Both JavaScript Engines and Specification

Park¹,

An²,

Youn³

et al. 2021

Preprint

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Modern programming follows the continuous integration (CI) and continuous deployment (CD) approach rather than the traditional waterfall model. Even the development of modern programming languages uses the CI/CD approach to swiftly provide new language features and to adapt to new development environments. Unlike in the conventional approach, in the modern CI/CD approach, a language specification is no more the oracle of the language semantics because both the specification and its implementations (interpreters or compilers) can co-evolve. In this setting, both the specification and implementations may have bugs, and guaranteeing their correctness is non-trivial.In this paper, we propose a novel N +1-version differential testing to resolve the problem. Unlike the traditional differential testing, our approach consists of three steps: 1) to automatically synthesize programs guided by the syntax and semantics from a given language specification, 2) to generate conformance tests by injecting assertions to the synthesized programs to check their final program states, 3) to detect bugs in the specification and implementations via executing the conformance tests on multiple implementations, and 4) to localize bugs on the specification using statistical information. We actualize our approach for the JavaScript programming language via JEST, which performs N +1-version differential testing for modern JavaScript engines and ECMAScript, the language specification describing the syntax and semantics of JavaScript in a natural language. We evaluated JEST with four JavaScript engines that support all modern JavaScript language features and the latest version of ECMAScript (ES11, 2020). JEST automatically synthesized 1,700 programs that covered 97.78% of syntax and 87.70% of semantics from ES11. Using the assertion-injected JavaScript programs, it detected 44 engine bugs in four different engines and 27 specification bugs in ES11.

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Formal Semantics Extraction from MIPS Instruction Manual

Trac

Ogawa

2020

Communications in Computer and Information Science

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Formal Semantics Extraction from Natural Language Specifications for ARM

Cited by 9 publications

References 21 publications

Knowledge Extraction from the Language Extended Lexicon Glossary Using Natural Language Processing

Knowledge Extraction from the Language Extended Lexicon Glossary Using Natural Language Processing

JEST: N+1-version Differential Testing of Both JavaScript Engines and Specification

Formal Semantics Extraction from MIPS Instruction Manual

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