Colin J. Burgess

Bio: Colin J. Burgess is an academic researcher. The author has contributed to research in topics: Hyper-heuristic & Software construction. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Using Artificial Intelligence to solve problems in Software Quality Management

Abstract: It is not uncommon for well established techniques in one particular research area to find good applications in a completely different field. This paper seeks to review instances where established techniques from the realm of Artificial intelligence have been applied to problems in Software Quality Management. This review is not meant to be exhaustive but will outline four areas where Artificial Intelligence techniques have been successfully used. These are:- 1. The application of genetic algorithms and other search techniques to aid the automatic generation of structural test data. 2. The application of genetic algorithms to the testing of real-time systems. 3. The use of influence diagrams to aid the management of software change. 4. Improving the cost estimation of software projects. d The overall purpose of the paper is to stimulate thought on how these, or other Artificial Intelligence techniques, could be used to either enhance the applications presented or solve other problems arising in Software Quality Management.

What is Quality 4.0? An exploratory sequential mixed methods study of Italian manufacturing companies

Abstract: The purpose of this paper is to contribute to the scientific debate on Quality 4.0 by exploring the main theoretical themes underpinning the Quality 4.0 model and how the model may be developed. An...

Towards Automated Software Testing : Techniques, Classifications and Frameworks

Abstract: Software is today used in more and different ways than ever before. From refrigerators and cars to space shuttles and smart cards. As such, most software, usually need to adhere to a specification, ...

A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

Abstract: Software quality is an important research area and has gain considerable attention from software engineering community in identification of priority quality attributes in software development process. This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. The proposed algorithm is able to improve the weaknesses in PQF model in updating and learning the important attributes for software quality assessment. The existing assessment techniques lack of the capability to rank the quality attributes and data learning which can enhance the quality assessment process. The aim of the study is to identify and propose the application of Artificial Intelligence (AI) technique for improving quality assessment technique in PQF model. Therefore, FRA using FRT was constructed and the performance of the FRA was evaluated. The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. The result shows that the performance of FRA correlates strongly to PQF model with 98% correlation compared to the Kolmogorov-Smirnov Correlation Based Filter (KSCBF) algorithm with 83% correlation. Statistical significance test was also performed with score of 0.052 compared to the KSCBF algorithm with score of 0.048. The result shows that the FRA was more significant than KSCBF algorithm. The main contribution of this research is on the implementation of FRT with proposed Most Priority of Features (MPF) calculation in FRA for attributes assessment. Overall, the findings and contributions can be regarded as a novel effort in software quality for attributes selection.

Intelligent software quality model: the theoretical framework

Abstract: Globally, software quality issues has increasingly been seen as a common strategic response for achieving competitiveness in business. It has been seen very important as the usage of software become very demanding. Software quality includes quality control tests, quality assurance and quality management. Currently, software quality models available were built based on static measurements of attributes and measures. Previous study has indicated that to ensure the quality of software meets the future requirements and needs, the new dynamic and intelligent software quality model has to be developed. This paper discusses the development of intelligent software quality model based on behavioral and human perspectives approach which enhances from Pragmatic Quality Factor (PQF) model as a benchmark of the quality assessment.

Development of a Dynamic and Intelligent Software Quality Model

Abstract: Previous research has identified factors and attributes for static quality model. This research aims to construct a dynamic and intelligent software quality model for effective software product assessment. Previous model of software quality and known as PQF model consists of two main quality attributes: the behavioural and the human aspect. These two components of quality produce a balance model between technical requirement and human factor. The proposed dynamic intelligent model of PQF (i-PQF) should capable to identify and recommend to the environment if there is any new attribute to be included in the model. This is done by integrating artificial intelligence technique and methods to produce a complete algorithm for assessing software product using intelligent model. It will be tested using a prototype. The new model is useful for organization in assessment of software products as well as to integrate in future researches as a quality benchmark.