Requirements Capture and Analysis in ASSERT(TM)

Crapo, Andrew; Moitra, Abha; McMillan, Craig

doi:10.1109/re.2017.54

Cited by 26 publications

(14 citation statements)

References 7 publications

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“…A natural language like input domain that we found in our survey is Structured English Language (4 primary studies, i. e., 3% of all primary studies), which overshadows the complexity of formal notations for engineers [55,56]. The ASSERT framework by Crapo et al [55] and Moitra et al [56,57], as well as the RailComplete framework by Luteberget and Johansen [54] accept Structured English Language as the input domain. To improve the interpretation of counterexamples, 5 primary studies (4% of all primary studies) use Structured English Language as the output domain.…”

Section: Input and Output Domains In Counterexample Explanationmentioning

confidence: 79%

“…Particularly, domain ontologies and vocabularies are used to enhance the counterexample with additional information to ease error comprehension. Crapo et al [55,107] and Moitra et al [56,57] use Requirements Analysis Engine (RAE) and Analysis of Semantic Specifications and Efficient generation of Requirements-based Tests (ASSERT) that accept a formal requirement in an easily understandable syntax by making use of a domain ontology. Further, they analyze an incomplete set of requirements and localizes the error by identifying the responsible requirements with an error marker.…”

Section: Information Enriching Textual Representations Of Counterexam...mentioning

confidence: 99%

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A systematic literature review on counterexample explanation

Kaleeswaran,

Nordmann,

Vogel

et al. 2022

Preprint

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Context: Safety is of paramount importance for cyber-physical systems in domains such as automotive, robotics, and avionics. Formal methods such as model checking are one way to ensure the safety of cyber-physical systems. However, adoption of formal methods in industry is hindered by usability issues, particularly the difficulty of understanding model checking results. Objective: We want to provide an overview of the state of the art for counterexample explanation by investigating the contexts, techniques, and evaluation of research approaches in this field. This overview shall provide an understanding of current and guide future research. Method: To provide this overview, we conducted a systematic literature review. The survey comprises 116 publications that address counterexample explanations for model checking. Results: Most primary studies provide counterexample explanations graphically or as traces, minimize counterexamples to reduce complexity, localize errors in the models expressed in the input formats of model checkers, support linear temporal logic or computation tree logic specifications, and use model checkers of the Smybolic Model Verifier family. Several studies evaluate their approaches in safety-critical domains with industrial applications. Conclusion: We notably see a lack of research on counterexample explanation that targets probabilistic and real-time systems, leverages the explanations to domain-specific models, and evaluates approaches in user studies. We conclude by discussing the adequacy of different types of explanations for users with varying domain and formal methods expertise, showing the need to support laypersons in understanding model checking results to increase adoption of formal methods in industry.

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Section: Input and Output Domains In Counterexample Explanationmentioning

confidence: 79%

Section: Information Enriching Textual Representations Of Counterexam...mentioning

confidence: 99%

A systematic literature review on counterexample explanation

Kaleeswaran,

Nordmann,

Vogel

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Several commercial tools are also available for formal requirements engineering. The ASSERT tool [8,30], proprietary to GE, uses an ontology-based approach both for formalizing domains through the language SADL, and the requirements themselves, through the language SRL. Requirements are in the form of assignments to attributes conditioned on (possibly temporal) Boolean conditions.…”

Section: Related Workmentioning

confidence: 99%

A compositional proof framework for FRETish requirements

Conrad

Titolo

Giannakopoulou

et al. 2022

Proceedings of the 11th ACM SIGPLAN International Conference on Certified Programs and Proofs

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Structured natural languages provide a trade space between ambiguous natural languages that make up most written requirements, and mathematical formal specifications such as Linear Temporal Logic. FRETish is a structured natural language for the elicitation of system requirements developed at NASA. The related open-source tool Fret provides support for translating FRETish requirements into temporal logic formulas that can be input to several verification and analysis tools. In the context of safety-critical systems, it is crucial to ensure that a generated formula captures the semantics of the corresponding FRETish requirement precisely. This paper presents a rigorous formalization of the FRETish language including a new denotational semantics and a proof of semantic equivalence between FRETish specifications and their temporal logic counterparts computed by Fret. The complete formalization and the proof have been developed in the Prototype Verification System (PVS) theorem prover.

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“…They validated their rules through a case study, and described their tool in another paper (Post and Hoenicke 2012). Crapo et al (2017) propose the Semantic Application Design Requirements Language which is a controlled natural language in English for writing functional requirements. Their language supports the mapping to first-order logic.…”

Section: Controlled Natural Languagesmentioning

confidence: 99%

On systematically building a controlled natural language for functional requirements

et al. 2021

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Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

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Requirements Capture and Analysis in ASSERT(TM)

Cited by 26 publications

References 7 publications

A systematic literature review on counterexample explanation

A systematic literature review on counterexample explanation

A compositional proof framework for FRETish requirements

On systematically building a controlled natural language for functional requirements

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