Fuzzy graphs: algebraic structure and syntactic recognition

Kalampakas, Antonios; Spartalis, Stefanos; Iliadis, Lazaros; Pimenidis, Elias

doi:10.1007/s10462-013-9412-0

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…Second, we have introduced Value Dependency Graphs (VDGs) based on fuzzy graphs [33,34] for modeling value-related requirement dependencies and capturing the imprecision associated with strengths of those dependencies. A modified version of the Floyd-Warshall algorithm [35] is devised to infer implicit value-related dependencies in polynomial time.…”

Section: Arxiv:200301824v1 [Csse] 3 Mar 2020mentioning

confidence: 99%

“…Hence, p i is calculated using fuzzy OR operator, which is to take supremum over the strengths of all ignored positive dependencies and selected neg-ative dependencies of r i in its corresponding value dependency graph. This is given in ( 26) and (33), where n denotes the number of requirements and x j specifies whether a requirement r j is selected (x j = 1) or not (x j = 0). Also, I i,j denotes the overall strength of all positive and negative value-related dependencies from r i to r j which is referred to as the overall influence of r j on the value of r i as given in (20).…”

Section: The Integer Programming Modelmentioning

confidence: 99%

“…(ii) Modeling value dependencies. We have demonstrated the use of fuzzy graphs (Zahedi et al, 2016;Rosenfeld, 1975) and their algebraic structure (Kalampakas et al, 2013) for modeling strengths and qualities of value dependencies and capturing the imprecision of those dependencies. On this basis, value dependencies and their strengths are modeled by fuzzy relations (Carlshamre et al, 2001;Liu & Yen, 1996) and their fuzzy membership functions respectively.…”

Section: Introductionmentioning

confidence: 99%

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Dependency-aware release planning for software projects using fuzzy graphs and integer programming

Mougouei

Powers

2019

IFS

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Software requirements selection aims to find an optimal subset of the requirements with the highest value while respecting the project constraints. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Such Value Dependencies, however, are imprecise and hard to capture. In this paper, we propose a method based on integer programming and fuzzy graphs to account for value dependencies and their imprecision in software requirements selection. The proposed method, referred to as Dependency-Aware Software Requirements Selection (DARS), is comprised of three components: (i) an automated technique for the identification of value dependencies from user preferences, (ii) a modeling technique based on fuzzy graphs that allows for capturing the imprecision of value dependencies, and (iii) an Integer Linear Programming (ILP) model that takes into account user preferences and value dependencies identified from those preferences to reduce the risk of value loss in software projects. Our work is verified by studying a realworld software project. The results show that our proposed method reduces the value loss in software projects and is scalable to large requirement sets.

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Section: Arxiv:200301824v1 [Csse] 3 Mar 2020mentioning

confidence: 99%

Section: The Integer Programming Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dependency-aware release planning for software projects using fuzzy graphs and integer programming

Mougouei

Powers

2019

IFS

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“…Eells measure [32] of causal strength is used to identify and quantify value dependencies based on user preferences for software requirements. Based on fuzzy graphs [33] [34], a modeling technique is proposed to model value dependencies among requirements; value dependencies are modeled as fuzzy relations [7], [35], [21], [36].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Based Optimization Method for Integrating Value Dependencies into Software Requirement Selection

Mougouei¹,

Powers²

2020

Preprint

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Software requirement selection aims to find an optimal subset of the requirements with the highest value while respecting the budget. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Existing requirement selection methods, however, do not consider Value Dependencies, thus increasing the risk of value loss. To address this, we have proposed Dependency-Aware Requirement Selection (DARS) method with two main components: (i) a fuzzy-based technique for identifying and modeling value dependencies, and (ii) an Integer Programming model that takes into account value dependencies in software requirement selection. We have further, proposed an alternative optimization model for situations where quantifying value dependencies is hard. The scalability of DARS and its effectiveness in reducing the risk of value loss are demonstrated through exhaustive simulations.

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“…First, we have demonstrated using algebraic structure of fuzzy graphs [46,47,48,49] to modeling valuerelated requirement dependencies and their strengths.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Selection of Interdependent Software Requirements Using Fuzzy Graphs

Mougouei

Powers

2017

Int. J. Fuzzy Syst.

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Software requirement selection is to find an optimal set of requirements that gives the highest value for a release of software while keeping the cost within the budget. However, value-related dependencies among software requirements may impact the value of an optimal set. Moreover, value-related dependencies can be of varying strengths. Hence, it is important to consider both the existence and the strengths of valuerelated dependencies during a requirement selection. The existing selection models however, either assume that software requirements are independent or they ignore strengths of requirement dependencies. This paper presents a cost-value optimization model that considers the impacts of value-related requirement dependencies on the value of selected requirements (optimal set). We have exploited algebraic structure of fuzzy graphs for modeling value-related requirement dependencies and their strengths. Validity and practicality of the work are verified through carrying out several simulations and studying a real world software project.

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Fuzzy graphs: algebraic structure and syntactic recognition

Cited by 8 publications

References 30 publications

Dependency-aware release planning for software projects using fuzzy graphs and integer programming

Dependency-aware release planning for software projects using fuzzy graphs and integer programming

A Fuzzy-Based Optimization Method for Integrating Value Dependencies into Software Requirement Selection

Modeling and Selection of Interdependent Software Requirements Using Fuzzy Graphs

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