Prediction of quality software quality indicators with applied modifications of integrated gradiates methods

• Autorzy: Shantyr Anton , Zinchenko Olha , Storchak Kamila , Bondarchuk Andrii , Pepa Yuriy

Identyfikatory

DOI

10.35784/iapgos.6892

Warianty tytułu

PL

Predykcja wskaźników jakości oprogramowania z zastosowaniem modyfikacji metod gradientów zintegrowanych

• Języki publikacji: EN

Abstrakty

EN

The article is devoted to modern software systems (SS) and improving their quality using machine learning methods, including the Integrated Gradients (IG) method. Key problems and limitation of IG use in real operating conditions of the SS, such as complexity of systems, correlation of variables and computing efficiency are considered. Ways to improve IG, including adaptive integration, spatial smoothing and use of weight factors, are proposed. Experimental results are described that confirm the effectiveness of the proposed modifications to improve the quality of the SS. Adaptive integration has achieved the best results (MAE 0.11), adaptability and interpretation.

PL

Artykuł poświęcony jest nowoczesnym systemom oprogramowania (SO) i poprawie ich jakości z wykorzystaniem metod uczenia maszynowego, w tym metody Zintegrowanych Gradientów (ZG). Rozważono kluczowe problemy i ograniczenia stosowania ZG w rzeczywistych warunkach działania SO, takie jak złożoność systemów, korelacja zmiennych i wydajność obliczeniowa. Zaproponowano sposoby ulepszenia ZG, w tym integrację adaptacyjną, wygładzanie przestrzenne i wykorzystanie współczynników wagowych. Opisano wyniki eksperymentalne, które potwierdzają skuteczność proponowanych modyfikacji w celu poprawy jakości SO. Integracja adaptacyjna osiągnęła najlepsze wyniki (MAE 0,11), zdolność adaptacji i interpretacji.

Słowa kluczowe

EN

software systems quality; machine learning; modeling; mathematical apparatus; innovation; quality assessment

PL

jakość systemów oprogramowania; uczenie maszynowe; modelowanie; aparat matematyczny; innowacje; ocena jakości

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2025
• Tom: T. 15, nr 2
• Strony: 139--146
• Opis fizyczny: Bibliogr. 28 poz., tab.

Twórcy

autor

Shantyr Anton

• State University of Information and Communication Technologies, Kyiv, Ukraine

• https://orcid.org/0000-0002-0466-3659

autor

Zinchenko Olha

• State University of Information and Communication Technologies, Kyiv, Ukraine

• https://orcid.org/0000-0002-3973-7814+

autor

Storchak Kamila

• State University of Information and Communication Technologies, Kyiv, Ukraine

• https://orcid.org/0000-0001-9295-4685+

autor

Bondarchuk Andrii

• Borys Grinchenko Kyiv Metropolitan University, Kyiv, Ukraine

• https://orcid.org/0000-0001-5124-5102+

autor

Pepa Yuriy

• State University of Information and Communication Technologies, Kyiv, Ukraine

• https://orcid.org/0000-0003-2073-1364+

Bibliografia

• [1] Akimova E. M., Kisel T. N.: Integrated application of strategic analysis methods. Kant 39(2), 2021, 10–16 [https://doi.org/10.24923/2222-243x.202139.2].
• [2] Butler M., Petre L., Sere K.: Integrated formal methods. Springer Berlin, Heidelberg 2002 [https://doi.org/10.1007/3-540-47884-1].
• [3] Dheeraj K. N., et al.: Crop quality prediction using ml and neural networks. International Journal on Cybernetics & Informatics 10(2), 2021, 7–11 [https://doi.org/10.5121/ijci.2021.100202].
• [4] Evgeni P.: Testability analysis methods. Evgeni P.: Digital integrated circuits. Design-for-Test Using Simulink and Stateflow. CRC Press, Boca Raton 2007 [https://doi.org/10.1201/9781315222110-4].
• [5] Fumagalli F., et al.: Incremental permutation feature importance (iPFI): Towards online explanations on data streams. Machine Learning 112, 2023, 4863–4903 [https://doi.org/10.1007/s10994-023-06385-y].
• [6] Gelperin D.: The power of integrated methods. ACM SIGSOFT Software Engineering Notes 19(4), 1994, 77–78 [https://doi.org/10.1145/190679.190687].
• [7] Gleirscher M., Foster S., Woodcock J.: New opportunities for integrated formal methods. ACM Computing Surveys 52(6), 2020, 1–36 [https://doi.org/10.1145/3357231].
• [8] Goldina A. A.: Methods of information disclosure in integrated reporting. Models, Systems, Networks in Economics, Technology, Nature and Society 3, 2021 [https://doi.org/10.21685/2227-8486-2021-3-2].
• [9] Hahn S.-J., Lee B.-H.: Quality evaluation to small scaled software implementation result. The Journal of Korean Institute of Information Technology 21(1), 2023, 1–10 [https://doi.org/10.14801/jkiit.2023.21.1.1].
• [10] Hooker J. N.: Optimization basics. Integrated methods for optimization. Springer US, 2011 [https://doi.org/10.1007/978-1-4614-1900-6_3].
• [11] Indi M. M., et al.: Evaluation of the effectiveness of technology-based project management systems for software development. Global International Journal of Innovative Research 1(2), 2023, 175–181 [https://doi.org/10.59613/global.v1i2.30].
• [12] Jha K. N., Chockalingam C. T.: Prediction of quality performance using artificial neural networks. Journal of Advances in Management Research 6(1), 2009, 70–86 [https://doi.org/10.1108/09727980910972172].
• [13] Kamaletdinov S., et al.: Evaluation of data quality based on Bayesian networks in railway rolling stock monitoring systems. E3S Web of Conferences 460, 2023, 04014 [https://doi.org/10.1051/e3sconf/202346004014].
• [14] Kaneko H.: Cross‐validated permutation feature importance considering correlation between features. Analytical Science Advances 3(9-10), 2022, 278–287 [https://doi.org/10.1002/ansa.202200018].
• [15] Kim S.-H.: Integrated development environment for java native methods. The Journal of the Korea Contents Association 10(7), 2010, 122–132 [https://doi.org/10.5392/jkca.2010.10.7.122].
• [16] Movsessian A., Cava D. G., Tcherniak D.: Interpretable machine learning in damage detection using shapley additive explanations. ASME J. Risk Uncertainty Part B. 8(2), 2022 [https://doi.org/10.1115/1.4053304].
• [17] Ni H., Yin H.: Exchange rate prediction using hybrid neural networks and trading indicators. Neurocomputing 72(13-15), 2009, 2815–2823 [https://doi.org/10.1016/j.neucom.2008.09.023].
• [18] Oden J. T., Robinson J.: Integrated theory of finite element methods. Mathematics of Computation 29(132), 1975, 1157 [https://doi.org/10.2307/2005763].
• [19] Pankov P. S., Tagaeva S. B.: Systems of differential equations and computer phenomena. Herald of Institute Mathematics of the National Academy of Sciences of the Kyrgyz Republic 2, 2020, 86–93 [https://doi.org/10.52448/16948173_2020_2_86].
• [20] Roser M. E., et al.: Investigating reasoning with multiple integrated neuroscientific methods. Frontiers in Human Neuroscience 9, 2015, [https://doi.org/10.3389/fnhum.2015.00041].
• [21] Smaling A.: Mixed, integrated, merged or hybrid methods? Kwalon 22(3), 2017. [https://doi.org/10.5117/2017.022.003.001].
• [22] ter Beek M. H., Monahan R.: Integrated formal methods. Springer International Publishing. 2022 [https://doi.org/10.1007/978-3-031-07727-2].
• [23] Wang S., Zhang Y.: Grad-CAM: Understanding AI models. Computers, Materials & Continua 76(2), 2023, 1321–1324 [https://doi.org/10.32604/cmc.2023.041419].
• [24] Yoon H.: A quantitative evaluation for usability under software quality models. International Journal on Recent and Innovation Trends in Computing and Communication 11(3), 2023, 24–29 [https://doi.org/10.17762/ijritcc.v11i3.6194].
• [25] Yue C., et al.: An entropy-based group decision-making approach for software quality evaluation. Expert Systems With Applications 238C, 2023, 121979 [https://doi.org/10.1016/j.eswa.2023.121979].
• [26] Zhang X., et al.: Informative data selection with uncertainty for multimodal object detection. IEEE Transactions on Neural Networks and Learning Systems 35(10), 2023, 13561-13573 [https://doi.org/10.1109/tnnls.2023.3270159].
• [27] Zhao J., et al.: Evaluating the impact of uncertainty visualization on model reliance. IEEE Transactions on Visualization and Computer Graphics 30(7), 2023, 4093–4107 [https://doi.org/10.1109/tvcg.2023.3251950].
• [28] Zhao Z., Severini T. A.: Integrated likelihood computation methods. Computational Statistics 32(1), 2016, 281–313 [https://doi.org/10.1007/s00180 016-0677-z].