Intelligent evaluation system for software quality measurement
Date
2022-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Federal University of Technology, Owerri.
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.
Description
Doctorate Degree in Computer Science.
Keywords
Software quality, Evaluation, Measurement, Metrics, Attributes, Testing, Reinforcement learning
Citation
Nwandu, I. C. ( 2022). Intelligent evaluation system for software quality measurement { Unpublished Doctoral Thesis}, Federal University of Technology, Owerri.