Integrating analytical statistical models, sequential pattern mining, and fuzzy set theory for advanced mobile app reliability assessment




mobile application, software development, reliability assessmen, the Corcoran model


The study presents a new method for evaluating the reliability of mobile applications using the Corcoran model. This model includes several aspects of reliability, including performance, reliability, availability, scalability, security, usability, and testability. The Corcoran model can be applied to evaluate mobile applications by analysing key reliability metrics. Using the model significantly improves the reliability assessment of applications compared to traditional methods, which are primarily focused on desktop and server configurations. The aim of the study is to offer a more optimised approach to evaluating the reliability of mobile applications. The paper examines the problems faced by mobile app developers. This study represents a new application of the Corcoran model in evaluating the reliability of mobile applications. This model is characterised by an emphasis on the use of quantitative statistics and the ability to provide an accurate estimate of the probability of failure without any inaccuracies, which distinguishes this model from other software reliability models. The paper suggests using a combination of analytical statistical models, data extraction methods such as sequential pattern analysis, and fuzzy set theory to implement the Corcoran model. The application of the methodology is demonstrated by studying software error reports and conducting a comprehensive statistical analysis of them. To improve the results of future research, the paper suggests making more extensive use of the Corcoran model in various mobile applications and environments. It is recommended to change the model to take into account the constantly changing characteristics of mobile applications and their increasing complexity. In addition, it is advisable to conduct additional research to improve the data mining methods used in the model and explore the possibility of integrating artificial intelligence for more advanced software reliability analysis. Applying the Corcoran model to the mobile app development process to evaluate reliability can significantly improve the quality of applications, leading to increased customer satisfaction and trust in mobile apps. This model can serve as a guide for developers and companies to evaluate and improve their applications, driving innovation and continuous improvement in the competitive mobile app sector.

Author Biographies

Oleksandr Shmatko, National Technical University "Kharkiv Polytechnic Institute"

PhD, Associate Professor, Associate Professor of the Department of software engineering and intelligent management technologies

Oleksii Kolomiitsev, National Technical University "Kharkiv Polytechnic Institute"

Dr. Sc. (Engineering), Professor, Professor of the Department of computer engineering and programming

Volodymyr Fedorchenko, Kharkiv National University of Radio Electronics

PhD, Associate Professor, Associate Professor of the Department of Electronic Computers

Iryna Mykhailenko, National Automobile and Road University

PhD, Associate Professor, Associate Professor of the Department of Higher Mathematics

Viacheslav Tretiak, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor, Senior Researcher



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How to Cite

Shmatko, O., Kolomiitsev, O., Fedorchenko, V., Mykhailenko, I., & Tretiak, V. (2023). Integrating analytical statistical models, sequential pattern mining, and fuzzy set theory for advanced mobile app reliability assessment. INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (4(26), 78–86.