Functionality semantics of predicate data flow diagram

Gao, Xiaolei; Miao, Huaikou; Ling, Liu

doi:10.1007/s11741-004-0070-y

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…There have been earlier attempts to formalize DFDs to reduce ambiguity and detect inconsistency and incompleteness (e.g., [27,21,19,9,20,26]), and some works provide formal techniques to support the definition of hierarchical DFDs (e.g., [31,10,27,24]). Representing DFDs in different levels of abstraction does not automatically guarantee consistency between the different abstract models.…”

Section: Related Workmentioning

confidence: 99%

Refining Privacy-Aware Data Flow Diagrams

Alshareef

Stucki

Schneider

2021

Lecture Notes in Computer Science

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Privacy, like security, is a non-functional property, yet most software design tools are focused on functional aspects, using for instance Data Flow Diagrams (DFDs). In previous work, a conceptual model was introduced where DFDs were extended into so-called Privacy-Aware Data Flow Diagrams (PA-DFDs) with the aim of adding specific privacy checks to existing DFDs. An implementation to add such automatic checks has also been developed. In this paper, we define the notion of refinement for both DFDs and PA-DFDs as a special type of structure-preserving map (or graph homomorphism). We also provide three algorithms to find, check and transform refinements, and we show that the standard diagram "transform→refine / refine→transform" commutes. We have implemented our algorithms in a proof-of-concept tool called DFD Refinery, and have applied it to realistic scenarios.

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Section: Related Workmentioning

confidence: 99%

Refining Privacy-Aware Data Flow Diagrams

Alshareef

Stucki

Schneider

2021

Lecture Notes in Computer Science

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“…[2] Its advantages are as follows: more errors were found in the early stage of the software life cycle, because in the creation of test model, test designers will find the original software requirements or model some inconsistent or incomplete information can easily achieve requirements tracking, because it is completely based on the model, and the model can be well back, according to the test case can find more defects than the traditional test case generation coverage criteria automatically from the formal model.…”

Section: Introductionmentioning

confidence: 99%

A New Test Case Generation Method Based on Data Flow Graph

Qu¹,

Wu²,

Yao³

et al. 2017

dtetr

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Abstract. It mainly solves the test lag, is not visual and the test efficiency is low, and relies on the test design of testers, and it cannot guarantee the quality of the test cases and cannot guarantee the quality of the test. According to the steps to establish the software data flow graph model to verify the model, the artificial point of the test items for planning. Finally fill in the contents of the test items and according to the algorithm to generate the corresponding test case collection, save to the database for management.

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Functionality semantics of predicate data flow diagram

Cited by 2 publications

References 6 publications

Refining Privacy-Aware Data Flow Diagrams

Refining Privacy-Aware Data Flow Diagrams

A New Test Case Generation Method Based on Data Flow Graph

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