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Software Quality Improvement Models Using Intelligent Technologies

Author(s) Hoa Khanh Dam
Country Australia
Abstract Software quality has become a critical determinant of the reliability, security, maintainability, scalability, and long-term sustainability of modern digital systems. The increasing complexity of software architectures, cloud environments, distributed applications, mobile platforms, artificial intelligence services, and interconnected digital ecosystems has made conventional quality-assurance approaches increasingly difficult to manage. Intelligent technologies provide new opportunities to strengthen software quality through automated defect prediction, machine learning, intelligent testing, anomaly detection, predictive maintenance, code analysis, process optimization, and adaptive quality monitoring. This study examines software quality improvement models using intelligent technologies and proposes an integrated conceptual framework for embedding intelligent quality management throughout the software development lifecycle. A conceptual and descriptive research design was adopted through a thematic synthesis of scholarly literature addressing software quality, intelligent software engineering, machine learning, automated testing, defect prediction, predictive analytics, software maintenance, and artificial intelligence-assisted development.
The analysis demonstrates that intelligent technologies can support software quality by identifying defects earlier, prioritizing testing activities, predicting failure-prone components, detecting anomalous behavior, and supporting continuous quality monitoring. Machine-learning models can analyze historical defect information and software metrics to identify components associated with higher defect probability. Natural language processing can assist in requirements analysis by identifying ambiguity, inconsistency, and missing information. Intelligent testing systems can prioritize test cases according to risk and historical failure patterns, while automated code analysis can identify potential weaknesses before deployment. The proposed quality improvement model connects requirements quality, architectural assessment, intelligent coding assistance, automated testing, predictive quality analysis, deployment monitoring, and continuous feedback.
The study further emphasizes that intelligent technologies should complement established software engineering principles rather than replace professional judgment. Significant challenges remain, including insufficient training data, model interpretability, false positives, integration with legacy development environments, computational costs, developer acceptance, cybersecurity risks, and the changing nature of software systems. The study concludes that intelligent technologies can transform software quality assurance from a predominantly reactive process into a proactive and continuously learning capability.
Organizations that integrate artificial intelligence, machine learning, predictive analytics, automated testing, and intelligent monitoring with established quality practices can develop more reliable, maintainable, secure, and adaptive digital systems.
Keywords Software Quality, Intelligent Technologies, Artificial Intelligence, Machine Learning, Software Testing, Defect Prediction, Predictive Analytics, Quality Assurance, Software Engineering, Intelligent Software Development.
Field Engineering
Published In Volume 2, Issue 5, September-October 2020
Published On 2020-10-02
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