Software Quality Measurement for Functional Suitability, Performance Efficiency, and Reliability Characteristics Using Analytical Hierarchy Process

Abstract

The quality model used in this paper is ISO 25010. Functional Suitability, Performance Efficiency, and Reliability are the characteristics to be used. The case study used is the ITS Academic Information System, and the method used for the basis of calculation is the AHP (Analytical Hierarchy Process) method. The initial stage is to make a list of questionnaire questions, which are then filled out by three stakeholders: experts, students, and developers. With the AHP method, experts will analyze the questionnaire results to determine the required weight. This weight is used to calculate the quality of the software. There are two types of software measurements: student questionnaires and developer questionnaires. These two questionnaires become data input. Automatic measurements are carried out on Time Behavior aspects, namely Response Time Testing. In the automatic measurement stage, the URL to be tested by the tester is used as data input. From this automatic measurement, we experimented with the response time of the destination URL to respond to requests and conversion results on a scale of one hundred. The final value of these two types of measurements will be used in several equations to get the final value of the quality of the software. The study results are in the form of automatic measuring instruments of software quality. The measurement results can be used as feedback in making improvements so that the quality value increases when measured. Regarding Functional Suitability, the ITS Academic Information System has provided features according to user needs. In the aspect of Performance Efficiency, the ITS Academic Information System can provide performance and performance according to user needs. Meanwhile, regarding reliability, the ITS Academic Information System can carry out a function under certain conditions and times

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

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