Integrating analytical statistical models, sequential pattern mining, and fuzzy set theory for advanced mobile app reliability assessment
DOI:
https://doi.org/10.30837/ITSSI.2023.26.078Keywords:
mobile application, software development, reliability assessmen, the Corcoran modelAbstract
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.
References
References
Mangla, M., Sharma, N., Mohanty, S. N. (2021), "A sequential ensemble model for software fault prediction", Innovations in Systems and Software Engineering, P. 1-8. DOI: https://doi.org/10.1007/s11334-021-00390-x
Khuat, T. T., Le, M. H. (2019), "Ensemble learning for software fault prediction problem with imbalanced data", International Journal of Electrical & Computer Engineering (2088-8708), Vol. 9, No 4. DOI: 10.11591/ijece.v9i4.pp3241-3246
Sales, A. M. A. et al. (2023), "Proposal of fault detection and diagnosis system architecture for residential air conditioners based on the Internet of Things", 2023 IEEE International Conference on Consumer Electronics (ICCE), P. 1-5. DOI: 10.1109/ICCE56470.2023.10043408.
Joorabchi, M. E., Mesbah, A., Kruchten, P. (2013), "Real challenges in mobile app development", 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, P. 15-24. DOI: 10.1109/ESEM.2013.9.
Heitkötter, H., Hanschke, S., Majchrzak, T. A. (2012), "Evaluating cross-platform development approaches for mobile applications", Web Information Systems and Technologies: 8th International Conference, WEBIST 2012, Revised Selected Papers 8, P. 120-138. DOI: https://doi.org/10.1007/978-3-642-36608-6_8
Zhang, H., Babar, M. A. (2013), "Systematic reviews in software engineering: An empirical investigation", Information and Software Technology, Vol. 55, No 7, P. 1341-1354. DOI: https://doi.org/10.1016/j.infsof.2012.09.008
Garousi, V., Mäntylä, M. V. (2016), "A systematic literature review of literature reviews in software testing", Information and Software Technology, Vol. 80, P. 195-216. DOI: https://doi.org/10.1016/j.infsof.2016.09.002
Felizardo, K. R. et al. (2017), "Defining protocols of systematic literature reviews in software engineering: a survey", 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), P. 202-209. DOI: 10.1109/SEAA.2017.17.
Pachouly, J. et al. (2022), "A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools", Engineering Applications of Artificial Intelligence, Vol. 111, 104773 р. DOI: https://doi.org/10.1016/j.engappai.2022.104773
Son, L. H., Pritam, N., Khari, M., Kumar, R., Phuong, P. T. M., & Thong, P. H. (2019), "Empirical study of software defect prediction: a systematic mapping", Symmetry, 11(2), 212 р. DOI: https://doi.org/10.3390/sym11020212
Li, Z., Jing, X. Y., Zhu, X. (2018), "Progress on approaches to software defect prediction", Iet Software, Vol. 12, No 3, P. 161-175. DOI: https://doi.org/10.1049/iet-sen.2017.0148
Zhou, T. et al. (2019), "Improving defect prediction with deep forest", Information and Software Technology, Vol. 114, P. 204-216. DOI: https://doi.org/10.1016/j.infsof.2019.07.003
Thota, M. K., Shajin, F. H., & Rajesh, P. (2020), "Survey on software defect prediction techniques", International Journal of Applied Science and Engineering, 17(4), P. 331-344. DOI: https://doi.org/10.6703/IJASE.202012_17(4).331
Singhal, S. et al. (2021), "Systematic literature review on test case selection and prioritization: A tertiary study", Applied Sciences, Vol. 11, No 24, P. 12121. DOI: https://doi.org/10.3390/app112412121
Shahrokni, A., Feldt R. (2013), "A systematic review of software robustness", Information and Software Technology, Vol. 55, No 1, P. 1-17. DOI: https://doi.org/10.1016/j.infsof.2012.06.002
Febrero, F., Calero, C., Moraga, M. Á. (2016), "Software reliability modeling based on ISO/IEC SQuaRE", Information and Software Technology, Vol. 70, P. 18-29. DOI: https://doi.org/10.1016/j.infsof.2015.09.006
Ali, S. et al. (2009), "A systematic review of the application and empirical investigation of search-based test case generation", IEEE Transactions on Software Engineering, Vol. 36, No 6, P. 742-762. DOI: 10.1109/TSE.2009.52.
Rathi, G., Tiwari, U. K., Singh, N. (2022), "Software Reliability: Elements, Approaches and Challenges", International Conference on Advances in Computing, Communication and Materials (ICACCM). P. 1-5. DOI: 10.1109/ICACCM56405.2022.10009422
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.