An Approach for Analyzing ISO / IEC 25010 Product Quality Requirements based on Fuzzy Logic and Likert Scale for Decision Support Systems

Abstract

Abstract-Decision Support Systems (DSS) are collaborative software systems that are built to support controlling of an organization in decision making process when faced with nonroutine problems in a specific application domain. It's important to measure portability, maintainability, security, reliability, functional suitability, performance efficiency, compatibility, and usability quality requirements of DSS properly. ISO / IEC 25010 which replaced ISO 9126, used for three different quality models for software products, such as: a) Quality in use model, b) Product quality model, and c) Data quality model. There is a lack of methodologies to measure and quantify these quality requirements. Fuzzy logic used to specify quality requirements of DSS, because it's an approach to computing based on degrees of truth, rather than true or false logics. Likert scale is a method in which it converts qualitative values into quantitative values to make a best statistical analysis. The measurement and quantification of quality requirements of DSS is a challenging task, because these quality requirements are in qualitative form and can't be represented in quantitative way. Although, several quality requirements methods for DSS have been proposed so far, but the research on analyzing quality requirements of DSS are still limited. In this paper, quantitative approach proposed for analyzing ISO / IEC 25010 product quality requirements based on fuzzy logic and likert scale for DSS which aims to quantify quality requirements. Moreover implemented proposed framework on a case study 'Internet Banking' and got data from 25 respondents i.e. System Analysts and Domain Experts of banking sector.

An ISO 25010 Based Quality Model for ERP Systems

“…Reliability factor expresses the capability of a system or software product to maintain its level of performance or specified functions under specified conditions for a specified time period. Four lower-factors are associated to reliability factor namely maturity, availability, fault tolerance and recoverability [13,14]. This quality factor has been adapted in our new ERP system quality model to assess the reliability of various functions and services that ERP systems provide in HEIs under certain stated conditions.…”

“…According to [14] usability factor describes the extent to which software or system product can be used to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use. The usability factor has set of lower-factors which include appropriateness recognizability, learnability, operability, user error protection, user interface aesthetics and accessibility [13]. In this study, supportability and searchability were added as lower-factors under usability to help evaluate the operations of ERP systems in HEIs.…”

Synergetic Evaluation of Project Portfolio Configuration Based on Data Envelopment Analysis

“…In order to get them, the peer experts are invited to score the value of the input indices ( , ) and output indices ( , ) and assess the highest capital investment of ( , ) and the optimum value of ( , ) by the Likert Scale Method [31,32]. The values of qualitative indices are shown as Table 1: In Table 1, the ρ represents the number of times that the index J has be scored, where =…”

Improving the Control Strategy of a Standalone PV Pumping System by Fuzzy Logic Technique

“…Figure 9 presents the working principle of an FLC and its different phases which are: The Fuzzification phase, applying predefined rules by the Inference Engine and finally the defuzzification phase. [ 20,21] The first step (Fuzzification) consists of transforming the numeric values on linguistic values that will be treated by the Inference engine (the second phase of FLC) which contains different inference rules, based on predefined fuzzy methods and experimental knowledge, to define a logical connection between the input and output variables by applying. After that, the computed fuzzy value is treated by the last part of the FLC, Defuzzification phase, in order to determine the final numeric value of the output solution.…”

“…The second input variable "dE", which is the variation of the error given by (20), in three fuzzy variables as shown in figure 11. Finally, the output variable ''Tem*",which is the computed torque reference, in seven fuzzy variables as shown in figure 12.…”