Formal Consistency Checking over Specifications in Natural Languages

Yan, Rong; Cheng, Chih-Hong; Zhang, Guangquan; Chai, Yesheng

doi:10.7873/date.2015.0452

Cited by 33 publications

(26 citation statements)

References 21 publications

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“…-Textual-based to formal notations:-most of the existing approaches adopt NLP techniques to parse the defined meta-model of the used textual format to generate the corresponding formal notation (e.g., [45]- [48]). Much progress has been witnessed within this type of transformation.…”

Section: Formality Levels Transformationsmentioning

confidence: 99%

Requirements Formality Levels Analysis and Transformation of Formal Notations into Semi-formal and Informal Notations (S)

Zaki¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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It is pivotal to have well-specified requirements to eliminate errors at an early stage of the system development life cycle. Some quality standards recommend the use of formal methods -mandate requirements to be expressed in formal notations -to detect errors. However, formal notations are not suitable for non-experts and may not be understood by all the stakeholder. To fix this, bidirectional transformations among requirement representation levels are required to maintain traceability and facilitate the communication of requirements among all the involved parties. This paper reflects on the different formality levels of requirements specifications including: informal, semi-formal, and formal notations. In addition, an automated multi-layer transformation approach is proposed to enable bi-directional transformation among requirements levels.

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Section: Formality Levels Transformationsmentioning

confidence: 99%

Requirements Formality Levels Analysis and Transformation of Formal Notations into Semi-formal and Informal Notations (S)

Zaki¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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“…By applying this grammar, requirements can be translated into logical abstractions. A similar grammar is proposed in [1]. It supports present, future and passive tenses with correct syntax according to English grammar.…”

Section: Semantic Role Labelingmentioning

confidence: 99%

“…It extends pure syntactic parsing by adding semantic reasoning and support for partitioning input and output variables. The second part uses synthesis techniques to determine if the requirements are consistent in terms of realizability [1]. Our framework differs from Yan2015 as follows: it creates abstraction logic by transforming propositional logic not only time constraints, it uses input data patterns to find logical inconsistencies and perform semantic role labeling.…”

Section: Related Workmentioning

confidence: 99%

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Detecting Logical Inconsistencies by Clustering Technique in Natural Language Requirements

Masuda

Matsuodani²,

Tsuda

2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn the early phases of the system development process, stakeholders exchange ideas and describe requirements in natural language. Requirements described in natural language tend to be vague and include logical inconsistencies, whereas logical consistency is the key to raising the quality and lowering the cost of system development. Hence, it is important to find logical inconsistencies in the whole requirements at this early stage. In verification and validation of the requirements, there are techniques to derive logical formulas from natural language requirements and evaluate their inconsistencies automatically. Users manually chunk the requirements by paragraphs. However, paragraphs do not always represent logical chunks. There can be only one logical chunk over some paragraphs on the other hand some logical chunks in one paragraph. In this paper, we present a practical approach to detecting logical inconsistencies by clustering technique in natural language requirements. Software requirements specifications (SRSs) are the target document type. We use k-means clustering to cluster chunks of requirements and develop semantic role labeling rules to derive "conditions" and "actions" as semantic roles from the requirements by using natural language processing. We also construct an abstraction grammar to transform the conditions and actions into logical formulas. By evaluating the logical formulas with input data patterns, we can find logical inconsistencies. We implemented our approach and conducted experiments on three case studies of requirements written in natural English. The results indicate that our approach can find logical inconsistencies.

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“…Yan et. al [37], in the tool SpecCC, transformed their own CNL into LTL, and synthesize the LTL specification using G4LTL in order to check for realizability.…”

Section: B Computer-processable Cnl Toolsmentioning

confidence: 99%

ReSA Tool: Structured Requirements Specification and SAT-based Consistency-checking

Mahmud¹,

Seceleanu²,

Ljungkrantz³

2016

Annals of Computer Science and Information Systems

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Abstract-Most industrial embedded systems requirements are specified in natural language, hence they can sometimes be ambiguous and error-prone. Moreover, employing an early-stage model-based incremental system development using multiple levels of abstraction, for instance via architectural languages such as EAST-ADL, calls for different granularity requirements specifications described with abstraction-specific concepts that reflect the respective abstraction level effectively.In this paper, we propose a toolchain for structured requirements specification in the ReSA language, which scales to multiple EAST-ADL levels of abstraction. Furthermore, we introduce a consistency function that is seamlessly integrated into the specification toolchain, for the automatic analysis of requirements logical consistency prior to their temporal logic formalization for full formal verification. The consistency check subsumes two parts: (i) transforming ReSA requirements specification into boolean expressions, and (ii) checking the consistency of the resulting boolean expressions by solving the satisfiability of their conjunction with the Z3 SMT solver. For validation, we apply the ReSA toolchain on an industrial vehicle speed control system, namely the Adjustable Speed Limiter.

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Formal Consistency Checking over Specifications in Natural Languages

Cited by 33 publications

References 21 publications

Requirements Formality Levels Analysis and Transformation of Formal Notations into Semi-formal and Informal Notations (S)

Requirements Formality Levels Analysis and Transformation of Formal Notations into Semi-formal and Informal Notations (S)

Detecting Logical Inconsistencies by Clustering Technique in Natural Language Requirements

ReSA Tool: Structured Requirements Specification and SAT-based Consistency-checking

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