Intelligent evaluation system for software quality measurement
| dc.contributor.author | Nwandu, Ikenna Caeser | |
| dc.date.accessioned | 2024-08-27T13:30:53Z | |
| dc.date.available | 2024-08-27T13:30:53Z | |
| dc.date.issued | 2022-12 | |
| dc.description | Doctorate Degree in Computer Science. | |
| dc.description.abstract | The concern about the large-scale and complexity of contemporary software cannot be over-emphasized. This is inclined to the assurance of standardized software quality which is essential for preventing disastrous effects of releasing fault-prone systems. This thesis designed an intelligent model that uses various metrics corresponding to six quality attributes (namely Reliability, Usability, Efficiency, Functionality, Maintainability and Portability) to measure the quality of software. This agreed to the assertion that software quality evaluation process is an instrument that observes the characteristics of a software product. In software engineering, the primary quality evaluation and assurance technique that establishes confidence over successful execution of software is termed software testing. Software testing usually identifies and applies metrics to software products in order to promote and assess their quality. This thesis designed an intelligent evaluation model in conformance with software testing principles. The objective of the model is to apply reinforcement learning in its software evaluation process to measure six software attributes in terms of speed of execution and to ensure optimal decision-making in the evaluation process, such that the model returns a reliable outcome. The model utilized a formulated model equation, whose input are the measured attributes, to achieve the evaluation. The model is developed using extreme programming principles, an agile framework whose operation is based on simplicity. It also adopted object-oriented analysis and design methodology which allowed the utilization of various artifacts including use cases, data flow, sequence, flowchart, entity-relationship and class diagrams to describe the architecture and functionality of the system. The model was implemented using Python programming language with the database design on MySQL platform. The model is further validated by comparing its performance measures on test data gotten from the functional information of Oil-palm Management Program and Estate CanePro. These tests data produced quality values of 0.9 and 1.0 respectively via the model equation. These results gave the indication that the resource software perform efficiently owing to the fact that the model’s value benchmark is best as it approaches unity. The result of comparing the outcomes showed that reinforcement learning makes software evaluation dynamic and precise. The results indicated that the model independently determines the strategies to follow during evaluations and the same set of data consistently gives the same outcome. The result also showed that the reliability of a software is directly proportional to its usability and maintainability. However, the result also showed that having a high portability value does not guarantee the reliability and/or maintainability of a given software. | |
| dc.description.sponsorship | Department of Computer Science, FUTO. | |
| dc.identifier.citation | Nwandu, I. C. ( 2022). Intelligent evaluation system for software quality measurement { Unpublished Doctoral Thesis}, Federal University of Technology, Owerri. | |
| dc.identifier.uri | https://repository.futo.edu.ng/handle/20.500.14562/1392 | |
| dc.language.iso | en | |
| dc.publisher | Federal University of Technology, Owerri. | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Software quality | |
| dc.subject | Evaluation | |
| dc.subject | Measurement | |
| dc.subject | Metrics | |
| dc.subject | Attributes | |
| dc.subject | Testing | |
| dc.subject | Reinforcement learning | |
| dc.title | Intelligent evaluation system for software quality measurement | |
| dc.type | Doctoral Thesis |