Qinghua Zhang

Bio: Qinghua Zhang is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Rough set & Fuzzy set. The author has an hindex of 16, co-authored 60 publications receiving 802 citations.

Effect of Project Management in Requirements Engineering and Requirements Change Management Processes for Global Software Development

Abstract: Presently, global software development (GSD) is growing very rapidly. However, it is not an easy and straightforward process. Requirements engineering (RE) and requirements change management (RCM) both are considered as very challenging activities due to demanding rich communications. Because it is necessary to address geographical and cultural differences in GSD, this requirement makes RE and RCM challenging. This paper investigates the importance of project management in RE and RCM processes. First, the frameworks with the phenomena of specialized project management are proposed for RE and RCM. Then, a survey and blind interviews of the experts are conducted to validate the proposed frameworks. Finally, statistical tools are applied to analyze the collected data. By utilizing the analyzed data, our results show the significant impacts of both frameworks (i.e., RE and RCM) in the GSD environment.

A Dynamic Three-way Decision Model based on the Updating of Attribute Values

Abstract: The three-way decision model is a topic of substantial research interest in the field of artificial intelligence, and many researchers have focused on to its feasibility and rationality. The tolerance and practicability of the three-way decision model are better than those of the two-way decision model. When the attribute value of each object in a domain is given, the formation of a three-way classification of the domain is a key issue. However, few studies have been conducted on establishing a three-way decision model with the given attribute values in the case where the number of objects in an accepted region is given. Therefore, in the model presented in this paper, both the uncertainty of attribute values and the cost of updating are fully considered. In this paper, first, a new concept of attribute ratio is defined to describe an object when the attribute value of the object is numerical, and then, a dynamic three-way decision model is established. Second, a feature extraction algorithm of attribute values is proposed, and a pair of decision thresholds of the dynamic three-way decision model is also obtained according to the given conditions. Then, in the case where the attribute values are updated, an example is provided to demonstrate how two-way classification results can be obtained in the dynamic decision-making process. Finally, the results of simulation experiments show that the proposed model is feasible and effective in practical applications. When the number of objects in an accepted region has been given, according to the updating strategy of attribute values, the three-way decision problems are successfully solved by the proposed model.

A Novel Three-way decision model with decision-theoretic rough sets using utility theory

Abstract: In the classical three-way decision (3WD) model with decision-theoretic rough sets (DTRSs), the classification correct rate (CCR) is an important issue. As one of the risk measurement methods, loss functions have been used to calculate thresholds. Using risk measurement methods relevant research has yielded many results. However, for improving the CCR, few research studies have focused on the risk measurement by considering the difference among the equivalence classes. In this paper, from the viewpoint of the difference among the equivalence classes, to improve the CCR, a novel model is proposed to derive the 3WD model with DTRSs by considering the new risk measurement functions through the utility theory. First, the weight of each attribute is calculated based on the knowledge distance. Then, with the aid of utility theory, the improved utility function, which can score the attribute values, is defined. Further, a reasonable model for constructing the utility-based scoring functions is proposed. Then, a decision procedure for calculating the exclusive thresholds is designed and the rules of three-way decisions (3WDs) are deduced. An example is presented to illustrate the proposed model and the trend of change for exclusive thresholds. Finally, our experimental results show that the performance of the proposed model is better than that of current existing models.

A Sequential Three-Way Decision Model With Intuitionistic Fuzzy Numbers

Abstract: Three-way decision model (3WDM) with decision-theoretical rough sets (DTRSs) always addresses precise cost parameters and precise attribute values in uncertain problem solving. However, intuitionistic fuzzy sets (IFSs), as extensions of fuzzy sets, are described by dual parameters, namely the membership and nonmembership degrees. Therefore, under intuitionistic fuzzy environments, the 3WDM confronted great challenges when intuitionistic fuzzy number (IFN) cost parameters and IFN attribute values arise together. In this paper, by considering the impacts of both the IFN cost parameters and the IFN attribute values, new 3WDMs with IFNs are constructed from the membership degree and nonmembership degree perspectives, respectively. Then optimistic and pessimistic 3WDMs with IFNs are, respectively, established based on different risk preferences for more accurate decisions. Subsequently, by introducing a sequential strategy, the presented models are applied to address attribute increments in practical problems. Finally, we present a numerical example and some experiments to validate the efficiency of sequential 3WDMs.

Granular Computing: Perspectives and Challenges

Abstract: Granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. Granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.

Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers

Abstract: This paper presents a new approach for the treatment of uncertainty which is based on interval-valued fuzzy-rough numbers (IVFRN). It is shown that by integrating the rough approach with the traditional fuzzy approach, the subjectivity that exists when defining the borders of fuzzy sets is eliminated. IVFRN make decision making possible using only the internal knowledge in the operative data available to the decision makers. In this way objective uncertainties are used and there is no need to rely on models of assumptions. Instead of different external parameters in the application of IVFRN, the structure of the given data is used. On this basis an original multi-criteria model was developed based on an IVFRN approach. In this multi-criteria model the traditional steps of the BWM (Best–Worst method) and MABAC (Multi-Attributive Border Approximation area Comparison) methods are modified. The model was tested and validated on a study of the optimal selection of fire fighting helicopters. Testing demonstrated that the model based on IVFRN enabled more objective expert evaluation of the criteria in comparison with traditional fuzzy and rough approaches. A sensitivity analysis of the IVFRN BWM-MABAC model was carried out by means of 57 scenarios, the results of which showed a high degree of stability. The results of the IVFRN model were validated by comparing them with the results of the fuzzy and rough extension of the MABAC, COPRAS and VIKOR models.