An Evidential Software Risk Evaluation Model

Abstract

Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

A new belief entropy and its application in software risk analysis

“…The final rankings of the risks are the same as in Ref. [3]. Therefore, the proposed entropy is effective.…”

“…We analysed the example in Ref. [3]. We replaced the Deng entropy with the proposed entropy and calculated the uncertainty.…”

A novel quantum belief entropy for uncertainty measure in complex evidence theory

“…In order to solve this problem, many relevant theories have been proposed, such as Z-number [2], intuitionistic fuzzy set [3], [4], rough set [5], [6], random permutation set [7], Dempster-Shafer evidence theory (DSET), etc [8]- [10]. Owing to its advantages in probability distribution of uncertain information, DSET and its branches [11] are widely used in many fields, such as decision-making [12]- [17], pattern recognition [18], [19], multi-source information fusion [20]- [22], classification decision [23], [24], reasoning [25]- [27], fault diagnosis [28], etc [29]- [34].…”

Complex interval number‐based uncertainty modeling method with its application in decision fusion

“…Therefore, various theories have been established to handle uncertainty, including Z number, 3 rough sets, 4,5 random permutation set, 6–8 intuitionistic fuzzy set, 9–11 and so on 12,13 . Moreover, these theories have been applied in various fields, including data fusion, 14,15 decision making, 16–19 casual inference 20 community detection, 21,22 risk assessment, 23,24 reliability analysis, 25,26 social network analysis, 27 classification, 28,29 fault diagnosis, 30 medical diagnosis, 31,32 and so on.…”

“…including data fusion, 14,15 decision making, [16][17][18][19] casual inference 20 community detection, 21,22 risk assessment, 23,24 reliability analysis, 25,26 social network analysis, 27 classification, 28,29 fault diagnosis, 30 medical diagnosis, 31,32 and so on.…”