DOI: https://doi.org/10.32515/2664-262X.2025.11(42).2.30-37
Удосконалена математична модель оцінювання якості вебдодатків з відкритим кодом
Про авторів
Приходько Андрій Сергійович , здобувач вищої освіти на третьому (освітньо-науковому) рівні, Одеський національний університет імені І. І. Мечникова, Одеса, Україна, ORCID: 0000-0002-7109-5508, e-mail: whiterandrek@gmail.com
Малахов Євгеній Валерійович, професор, доктор технічних наук, завідувач кафедри математичного забезпечення комп'ютерних систем, Одеський національний університет імені І. І. Мечникова, Одеса, Україна, ORCID: 0000-0002-9314-6062, e-mail: eugene.malakhov@onu.edu.ua
Анотація
Робота присвячена підвищенню достовірності оцінок якості вебзастосунків з відкритим кодом, розроблених із застосуванням фреймворків PHP, за метриками RFC, CBO та WMC. Наукова новизна результатів дослідження полягає в удосконаленні математичної моделі у вигляді довірчих інтервалів та інтервалів прогнозування нелінійних регресій для метрик RFC, CBO та WMC на рівні додатку для оцінювання якості ПЗ на основі тривимірного перетворення Бокса-Кокса, яка, на відміну від існуючих моделей, дозволяє здійснювати відповідне оцінювання для веб-додатків з відкритим кодом, розроблених за допомогою PHP фреймворків. Отримані результати мають наукове та практичне значення для оцінювання якості веб-додатків з відкритим кодом, розроблених з використанням таких відомих PHP фреймворків, як CakePHP, CodeIgniter, Laravel, Symfony та Yii, за даними їхніх метрик RFC, CBO та WMC на рівні додатку та можуть бути застосовані для створення нових моделей та алгоритмів для оцінювання якості веб-додатків.
Ключові слова
математична модель, якість, програмне забезпечення, веб-додаток, нормалізуюче перетворення, нелінійна регресія, програмна метрика
Повний текст:
PDF
Посилання
1. Madaehoh, A., & Senivongse, T. (2022). OSS-AQM: An open-source software quality model for automated quality measurement. Data and Software Engineering (ICoDSE): proceedings of the 2022 International Conference (pp. 126-131). Denpasar, Indonesia. https://doi.org/10.1109/ICoDSE56892.2022.9972135
2. Chen, C., Shoga, M., & Boehm, B. (2019). Exploring the dependency relationships between software qualities. Software Quality, Reliability and Security Companion (QRS-C): proceedings of the 2019 IEEE 19th International Conference (pp. 105-108). Sofia, Bulgaria. https://doi.org/10.1109/QRS-C.2019.00032
3. Bombiri, O., Poda, P., & Ouedraogo, T.F. (2023). Application of machine learning in software quality: a Mini-review. Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD): proceedings of 2023 IEEE Multi-conference (pp. 1-7). Bobo-Dioulasso, Burkina Faso. https://doi.org/10.1109/MNE3SD57078.2023.10079800
4. Gao C., Luo W., Wang J. et al. (2022). Software quality evaluation model based on multiple linear regression and fuzzy comprehensive evaluation method. Dependable Systems and Their Applications (DSA): proceedings of the 2022 9th International Conference (pp. 383-389). Wulumuqi, China. https://doi.org/10.1109/DSA56465.2022.00058
5. Deshpande, M.V., Soitkar, A., Tripathi, D. R. et al. (2023). Ensuring web application quality: The role of software testing as a form of quality assurance. ICT in Business Industry & Government (ICTBIG): proceedings of the 2023 IEEE International Conference (pp. 1-6). Indore, India. https://doi.org/10.1109/ICTBIG59752.2023.10456277
6. Fizza, K., Banerjee, A., Jayaraman, P.P. et al. (2023). A survey on evaluating the quality of autonomic Internet of things applications. IEEE Communications Surveys & Tutorials, 1 (25), 567-590. https://doi.org/10.1109/COMST.2022.3205377
7. Shyamal, D.K.K., Asanka, P.P.G.D., & Wickramaarachchi, D. (2023). A comprehensive approach to evaluating software code quality through a flexible quality model. Smart Computing and Systems Engineering (SCSE): proceedings of the 2023 International Research Conference (pp. 1-8). Kelaniya, Sri Lanka. https://doi.org/10.1109/SCSE59836.2023.10215004
8. Prykhodko, S. (2024). Evaluating quality of software systems by the confidence and prediction intervals of regressions for RFC, CBO, and WMC metrics. WSEAS Transactions on Systems, 23, 322-330. https://doi.org/10.37394/23202.2024.23.36
9. Chidamber, S.R., & Kemerer, C.F. (1991). Towards a metrics suite for object oriented design. ACM SIGPLAN Notices, 11 (26), 197–211. https://doi.org/10.1145/118014.117970
10. Brito e Abreu, F., Melo, W. (1996). Evaluating the impact of object-oriented design on software quality. Software Metrics: proceedings of the 3rd International Symposium (pp. 90-99). Berlin, Germany. https://doi.org/10.1109/METRIC.1996.492446
11. Prykhodko, S., & Prykhodko, N. (2022). A technique for detecting software quality based on the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Sciences and Information Technologies (CSIT): proceedings of the 2022 IEEE 17th International Conference (pp. 499-502). Lviv, Ukraine. https://doi.org/10.1109/CSIT56902.2022.10000532
12. Prykhodko, S., & Prykhodko, N. (2023). Estimating quality of open-source Kotlin-based apps by the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Science and Information Technologies (CSIT): proceedings of the 2023 IEEE 18th International Conference (pp. 1-4). Lviv, Ukraine. https://doi.org/10.1109/CSIT61576.2023.10324187
13. Saravanan, N., Dharanya, C., Dhina, M. et al. (2024). A novel approach to predict the defect density in software application using linear regression algorithm. Science Technology Engineering and Management (ICSTEM): proceedings of the 2024 International Conference (pp. 1-5). Coimbatore, India. https://doi.org/10.1109/ICSTEM61137.2024.10560850
14. Prykhodko, A.S., Malakhov, E.V. (2024). Determining object-oriented design complexity due to the identification of classes of open-source web applications created using PHP frameworks. Radio Electronics, Computer Science, Control, 2 (69), 160–166. https://doi.org/10.15588/1607-3274-2024-2-16
Пристатейна бібліографія
1. Madaehoh A., Senivongse T. OSS-AQM: An open-source software quality model for automated quality measurement. Data and Software Engineering (ICoDSE) : proceedings. The 2022 International Conference. Denpasar, Indonesia: 2022. P. 126-131. DOI: 10.1109/ICoDSE56892.2022.9972135
2. Chen C., Shoga M., Boehm B. Exploring the dependency relationships between software qualities. Software Quality, Reliability and Security Companion (QRS-C) : proceedings. the 2019 IEEE 19th International Conference. Sofia, Bulgaria: 2019. P. 105-108. DOI: 10.1109/QRS-C.2019.00032
3. Bombiri O., Poda P., Ouedraogo T.F. Application of machine learning in software quality: a Mini-review. Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD) : proceedings. The 2023 IEEE Multi-conference. Bobo-Dioulasso, Burkina Faso: 2023. P. 1-7. DOI: 10.1109/MNE3SD57078.2023.10079800
4. Gao C., Luo W., Wang J. et al. Software quality evaluation model based on multiple linear regression and fuzzy comprehensive evaluation method. Dependable Systems and Their Applications (DSA) : proceedings. The 2022 9th International Conference. Wulumuqi, China: 2022. P. 383-389. DOI: 10.1109/DSA56465.2022.00058
5. Deshpande M.V., Soitkar A., Tripathi D. R. et al. Ensuring web application quality: The role of software testing as a form of quality assurance. ICT in Business Industry & Government (ICTBIG) : proceedings. The 2023 IEEE International Conference. Indore, India: 2023. P. 1-6. DOI: 10.1109/ICTBIG59752.2023.10456277
6. Fizza K., Banerjee A., Jayaraman P.P. et al. A survey on evaluating the quality of autonomic Internet of things applications. IEEE Communications Surveys & Tutorials. 2023. Vol. 25, No. 1. P. 567-590. DOI: 10.1109/COMST.2022.3205377
7. Shyamal D.K.K., Asanka P.P.G.D., Wickramaarachchi D. A comprehensive approach to evaluating software code quality through a flexible quality model. Smart Computing and Systems Engineering (SCSE) : proceedings. The 2023 International Research Conference. Kelaniya, Sri Lanka: 2023. P. 1-8. DOI: 10.1109/SCSE59836.2023.10215004
8. Prykhodko S. Evaluating quality of software systems by the confidence and prediction intervals of regressions for RFC, CBO, and WMC metrics. WSEAS Transactions on Systems. 2024. Vol. 23. P. 322-330. DOI: 10.37394/23202.2024.23.36
9. Chidamber S.R., Kemerer C.F. Towards a metrics suite for object oriented design. ACM SIGPLAN Notices. 1991. Vol. 26, Issue 11. P. 197–211. DOI: 10.1145/118014.117970
10. Brito e Abreu F., Melo W. Evaluating the impact of object-oriented design on software quality. Software Metrics : proceedings. The 3rd International Symposium. Berlin, Germany: 1996. P. 90-99. DOI: 10.1109/METRIC.1996.492446
11. Prykhodko S., Prykhodko N. A technique for detecting software quality based on the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Sciences and Information Technologies (CSIT) : proceedings. The 2022 IEEE 17th International Conference. Lviv, Ukraine: 2022. P. 499-502. DOI: 10.1109/CSIT56902.2022.10000532
12. Prykhodko S., Prykhodko N. Estimating quality of open-source Kotlin-based apps by the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Science and Information Technologies (CSIT) : proceedings. The 2023 IEEE 18th International Conference. Lviv, Ukraine: 2023. P. 1-4. DOI: 10.1109/CSIT61576.2023.10324187
13. Saravanan N., Dharanya C., Dhina M. et al. A novel approach to predict the defect density in software application using linear regression algorithm. Science Technology Engineering and Management (ICSTEM) : proceedings. The 2024 International Conference. Coimbatore, India: 2024. P. 1-5. DOI: 10.1109/ICSTEM61137.2024.10560850
14. Prykhodko A.S., Malakhov E.V. Determining object-oriented design complexity due to the identification of classes of open-source web applications created using PHP frameworks. Radio Electronics, Computer Science, Control. 2024. Vol. 69, No. 2. P. 160–166. DOI: 10.15588/1607-3274-2024-2-16
Copyright (c) 2025 А. С. Приходько, Є. В. Малахов
Удосконалена математична модель оцінювання якості вебдодатків з відкритим кодом
Про авторів
Приходько Андрій Сергійович , здобувач вищої освіти на третьому (освітньо-науковому) рівні, Одеський національний університет імені І. І. Мечникова, Одеса, Україна, ORCID: 0000-0002-7109-5508, e-mail: whiterandrek@gmail.com
Малахов Євгеній Валерійович, професор, доктор технічних наук, завідувач кафедри математичного забезпечення комп'ютерних систем, Одеський національний університет імені І. І. Мечникова, Одеса, Україна, ORCID: 0000-0002-9314-6062, e-mail: eugene.malakhov@onu.edu.ua
Анотація
Ключові слова
Повний текст:
PDFПосилання
1. Madaehoh, A., & Senivongse, T. (2022). OSS-AQM: An open-source software quality model for automated quality measurement. Data and Software Engineering (ICoDSE): proceedings of the 2022 International Conference (pp. 126-131). Denpasar, Indonesia. https://doi.org/10.1109/ICoDSE56892.2022.9972135
2. Chen, C., Shoga, M., & Boehm, B. (2019). Exploring the dependency relationships between software qualities. Software Quality, Reliability and Security Companion (QRS-C): proceedings of the 2019 IEEE 19th International Conference (pp. 105-108). Sofia, Bulgaria. https://doi.org/10.1109/QRS-C.2019.00032
3. Bombiri, O., Poda, P., & Ouedraogo, T.F. (2023). Application of machine learning in software quality: a Mini-review. Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD): proceedings of 2023 IEEE Multi-conference (pp. 1-7). Bobo-Dioulasso, Burkina Faso. https://doi.org/10.1109/MNE3SD57078.2023.10079800
4. Gao C., Luo W., Wang J. et al. (2022). Software quality evaluation model based on multiple linear regression and fuzzy comprehensive evaluation method. Dependable Systems and Their Applications (DSA): proceedings of the 2022 9th International Conference (pp. 383-389). Wulumuqi, China. https://doi.org/10.1109/DSA56465.2022.00058
5. Deshpande, M.V., Soitkar, A., Tripathi, D. R. et al. (2023). Ensuring web application quality: The role of software testing as a form of quality assurance. ICT in Business Industry & Government (ICTBIG): proceedings of the 2023 IEEE International Conference (pp. 1-6). Indore, India. https://doi.org/10.1109/ICTBIG59752.2023.10456277
6. Fizza, K., Banerjee, A., Jayaraman, P.P. et al. (2023). A survey on evaluating the quality of autonomic Internet of things applications. IEEE Communications Surveys & Tutorials, 1 (25), 567-590. https://doi.org/10.1109/COMST.2022.3205377
7. Shyamal, D.K.K., Asanka, P.P.G.D., & Wickramaarachchi, D. (2023). A comprehensive approach to evaluating software code quality through a flexible quality model. Smart Computing and Systems Engineering (SCSE): proceedings of the 2023 International Research Conference (pp. 1-8). Kelaniya, Sri Lanka. https://doi.org/10.1109/SCSE59836.2023.10215004
8. Prykhodko, S. (2024). Evaluating quality of software systems by the confidence and prediction intervals of regressions for RFC, CBO, and WMC metrics. WSEAS Transactions on Systems, 23, 322-330. https://doi.org/10.37394/23202.2024.23.36
9. Chidamber, S.R., & Kemerer, C.F. (1991). Towards a metrics suite for object oriented design. ACM SIGPLAN Notices, 11 (26), 197–211. https://doi.org/10.1145/118014.117970
10. Brito e Abreu, F., Melo, W. (1996). Evaluating the impact of object-oriented design on software quality. Software Metrics: proceedings of the 3rd International Symposium (pp. 90-99). Berlin, Germany. https://doi.org/10.1109/METRIC.1996.492446
11. Prykhodko, S., & Prykhodko, N. (2022). A technique for detecting software quality based on the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Sciences and Information Technologies (CSIT): proceedings of the 2022 IEEE 17th International Conference (pp. 499-502). Lviv, Ukraine. https://doi.org/10.1109/CSIT56902.2022.10000532
12. Prykhodko, S., & Prykhodko, N. (2023). Estimating quality of open-source Kotlin-based apps by the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Science and Information Technologies (CSIT): proceedings of the 2023 IEEE 18th International Conference (pp. 1-4). Lviv, Ukraine. https://doi.org/10.1109/CSIT61576.2023.10324187
13. Saravanan, N., Dharanya, C., Dhina, M. et al. (2024). A novel approach to predict the defect density in software application using linear regression algorithm. Science Technology Engineering and Management (ICSTEM): proceedings of the 2024 International Conference (pp. 1-5). Coimbatore, India. https://doi.org/10.1109/ICSTEM61137.2024.10560850
14. Prykhodko, A.S., Malakhov, E.V. (2024). Determining object-oriented design complexity due to the identification of classes of open-source web applications created using PHP frameworks. Radio Electronics, Computer Science, Control, 2 (69), 160–166. https://doi.org/10.15588/1607-3274-2024-2-16
Пристатейна бібліографія
1. Madaehoh A., Senivongse T. OSS-AQM: An open-source software quality model for automated quality measurement. Data and Software Engineering (ICoDSE) : proceedings. The 2022 International Conference. Denpasar, Indonesia: 2022. P. 126-131. DOI: 10.1109/ICoDSE56892.2022.9972135
2. Chen C., Shoga M., Boehm B. Exploring the dependency relationships between software qualities. Software Quality, Reliability and Security Companion (QRS-C) : proceedings. the 2019 IEEE 19th International Conference. Sofia, Bulgaria: 2019. P. 105-108. DOI: 10.1109/QRS-C.2019.00032
3. Bombiri O., Poda P., Ouedraogo T.F. Application of machine learning in software quality: a Mini-review. Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD) : proceedings. The 2023 IEEE Multi-conference. Bobo-Dioulasso, Burkina Faso: 2023. P. 1-7. DOI: 10.1109/MNE3SD57078.2023.10079800
4. Gao C., Luo W., Wang J. et al. Software quality evaluation model based on multiple linear regression and fuzzy comprehensive evaluation method. Dependable Systems and Their Applications (DSA) : proceedings. The 2022 9th International Conference. Wulumuqi, China: 2022. P. 383-389. DOI: 10.1109/DSA56465.2022.00058
5. Deshpande M.V., Soitkar A., Tripathi D. R. et al. Ensuring web application quality: The role of software testing as a form of quality assurance. ICT in Business Industry & Government (ICTBIG) : proceedings. The 2023 IEEE International Conference. Indore, India: 2023. P. 1-6. DOI: 10.1109/ICTBIG59752.2023.10456277
6. Fizza K., Banerjee A., Jayaraman P.P. et al. A survey on evaluating the quality of autonomic Internet of things applications. IEEE Communications Surveys & Tutorials. 2023. Vol. 25, No. 1. P. 567-590. DOI: 10.1109/COMST.2022.3205377
7. Shyamal D.K.K., Asanka P.P.G.D., Wickramaarachchi D. A comprehensive approach to evaluating software code quality through a flexible quality model. Smart Computing and Systems Engineering (SCSE) : proceedings. The 2023 International Research Conference. Kelaniya, Sri Lanka: 2023. P. 1-8. DOI: 10.1109/SCSE59836.2023.10215004
8. Prykhodko S. Evaluating quality of software systems by the confidence and prediction intervals of regressions for RFC, CBO, and WMC metrics. WSEAS Transactions on Systems. 2024. Vol. 23. P. 322-330. DOI: 10.37394/23202.2024.23.36
9. Chidamber S.R., Kemerer C.F. Towards a metrics suite for object oriented design. ACM SIGPLAN Notices. 1991. Vol. 26, Issue 11. P. 197–211. DOI: 10.1145/118014.117970
10. Brito e Abreu F., Melo W. Evaluating the impact of object-oriented design on software quality. Software Metrics : proceedings. The 3rd International Symposium. Berlin, Germany: 1996. P. 90-99. DOI: 10.1109/METRIC.1996.492446
11. Prykhodko S., Prykhodko N. A technique for detecting software quality based on the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Sciences and Information Technologies (CSIT) : proceedings. The 2022 IEEE 17th International Conference. Lviv, Ukraine: 2022. P. 499-502. DOI: 10.1109/CSIT56902.2022.10000532
12. Prykhodko S., Prykhodko N. Estimating quality of open-source Kotlin-based apps by the confidence and prediction intervals of nonlinear regression for RFC metric. Computer Science and Information Technologies (CSIT) : proceedings. The 2023 IEEE 18th International Conference. Lviv, Ukraine: 2023. P. 1-4. DOI: 10.1109/CSIT61576.2023.10324187
13. Saravanan N., Dharanya C., Dhina M. et al. A novel approach to predict the defect density in software application using linear regression algorithm. Science Technology Engineering and Management (ICSTEM) : proceedings. The 2024 International Conference. Coimbatore, India: 2024. P. 1-5. DOI: 10.1109/ICSTEM61137.2024.10560850
14. Prykhodko A.S., Malakhov E.V. Determining object-oriented design complexity due to the identification of classes of open-source web applications created using PHP frameworks. Radio Electronics, Computer Science, Control. 2024. Vol. 69, No. 2. P. 160–166. DOI: 10.15588/1607-3274-2024-2-16
Copyright (c) 2025 А. С. Приходько, Є. В. Малахов