Asia Pacific University Library catalogue


ENHANCING SOFTWARE QUALITY USING ARTIFICIAL NEURAL NETWORKS TO SUPPORT SOFTWARE REFACTORING / (Record no. 383358)

000 -LEADER
fixed length control field 02554nam a2200229 4500
003 - CONTROL NUMBER IDENTIFIER
control field APU
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230626103020.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200227b2019 ||||| |||| 00| 0 eng d
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number PM-32-14
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name PARVEENA SANDRASEGARAN (TP039382)
9 (RLIN) 45476
245 10 - TITLE STATEMENT
Title ENHANCING SOFTWARE QUALITY USING ARTIFICIAL NEURAL NETWORKS TO SUPPORT SOFTWARE REFACTORING /
Statement of responsibility, etc PARVEENA SANDRASEGARAN.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Kuala Lumpur :
Name of publisher, distributor, etc Asia Pacific University,
Date of publication, distribution, etc 2019.
300 ## - PHYSICAL DESCRIPTION
Extent xiv, 45 pages :
Other physical details illustrations ;
Dimensions 30 cm.
502 ## - DISSERTATION NOTE
Dissertation note A thesis submitted in fulfilment of the requirements for the award of the degree of MSc. in Software Engineering (UCMF1808BSE).
520 ## - SUMMARY, ETC.
Summary, etc Current trends of software refactoring involve tools and techniques to eliminate code smells that hinder the software from achieving quality goals. This is carried out manually as the developer is required to analyse the system in order to identify how a particular quality attribute is being affected. This approach to software development is inefficient as a majority of software engineers lack this skill and it prolongs the time allocated for the software’s implementation and maintenance. This dissertation outlines the need for Artificial Neural Networks (ANN) to support software refactoring in order to enhance the system’s quality. This justification is emphasized by means of illustrating the issues that arise when software quality is affected by the presence of code smells that have been overlooked by the developers. By adhering to a research methodology that comprises of SEVEN major phases, an ANN model is able to measure software quality in terms of efficiency, maintainability, and reusability. This calculation is based on inputs that are generated through SciTools whereby an application is decomposed into metric parameters such as Cyclomatic Complexity (CC). The results of the quality of ELEVEN JAVA projects were quantified in order to further analyse patterns of code smells; this provides an insight on how the model may be utilized to enhance software quality. Furthermore, the performance of the model is evaluated relative to other Machine Learning (ML) models.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer software
9 (RLIN) 45477
General subdivision Development.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Software refactoring.
9 (RLIN) 46691
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Neural networks (Computer science).
9 (RLIN) 46653
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Dr. Sivakumar Vengusamy
Relator term Supervisor.
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856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://cas.apiit.edu.my/cas/login?service=https://library.apu.edu.my/apres/
Link text Available in APres
Public note - Requires login to view full text.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Masters Theses
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Use restrictions Not for loan Collection code Home library Current library Shelving location Date acquired Full call number Barcode Date last seen Copy number Koha item type Public note
Not Withdrawn Available   Not Damaged Restricted access Not for loan Masters Theses APU Library APU Library Reference Collection 15/12/2020 PM-32-14 00018473 15/12/2020 1 Reference Available in APres