1. SISN
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  3. Volume 15, 2024
  4. Principles of Creating Multi-objective Quality Models for Software Systems

Principles of Creating Multi-objective Quality Models for Software Systems

SISN.
2024;
Volume 15
: pp. 115 - 133
https://doi.org/10.23939/sisn2024.15.115
Authors:
  1. Anton Shantyr
1
State University of Information and Communication Technologies, Department of Artificial Intelligence

This article proposes an original approach at a mathematical level to the creation of multi- objective quality models for software systems. The research is based on the study and generalization of quality modeling trends of software systems and user needs in order to determine optimal principles for constructing such  models. The article provides mathematical explanations that play a key role in identifying and formalizing the principles of creating multi-objective quality models of software. An important aspect is the consideration of quality model construction principles at the mathematical level, allowing for a more precise assessment and analysis of various aspects of software quality. The research results indicate that incorporating mathematical principles into the creation of multi-objective quality models for software systems can have a significant practical impact. It is established that on a practical level of developing multi-objective quality models for software systems, consideration of the principles of creating multi-objective quality models can have numerous practical implications. Specifically, the application of metrics within established principles allows for a comprehensive view of software quality and identifies areas requiring attention and improvement. This helps developers and software quality engineers make informed decisions regarding system improvement and optimization. The research has shown that creating high-quality models of quality requires attention to various aspects, from user needs to testing and continuous improvement, as well as the use of mathematical methods for their formalization and analysis. The developed principles of creating multi-objective quality models at the level of mathematical models allow the use of these models to assess and analyze various aspects of software quality, representing each model using a corresponding function that determines the relationship between quality metrics and the quality of the software itself. It is expected that further development and implementation of these principles will contribute to improving software development processes and ensure high quality of the resulting software.

software
quality metrics
user needs
information technology
agile methodology
system support
quality standards
mathematical framework
  1. Abbas, S., Aftab, S., Adnan Khan, M., M. Ghazal, T., Al Hamadi, H., & Yeob Yeun, C. (2023). Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System. Computers, Materials & Continua, 75(3), 6083–6100. https://doi.org/10.32604/cmc.2023.037933
  2. Aftab, S., Abbas, S., Ghazal, T. M., Ahmad, M., Hamadi, H. A., Yeun, C. Y., & Khan, M. A. (2023). A cloud-based   software   defect   prediction   system   using   data   and   decision-level    machine    learning fusion. Mathematics, 11(3), 632. https://doi.org/10.3390/math11030632
  3. Arcos-Medina, G., & Mauricio, D. (2020). The influence of the application of agile practices in software quality based on ISO/IEC 25010 standard. International Journal of Information Technologies and Systems Approach, 13(2), 27–53. https://doi.org/10.4018/ijitsa.2020070102
  4. Azar, D., Harmanani, H., & Korkmaz, R. (2009). A hybrid heuristic approach to optimize rule-based software      quality      estimation      models. Information      and      Software      Technology, 51(9),       1365– 1376.    https://doi.org/10.1016/j.infsof.2009.05.003
  5. Bharathi, R., & Selvarani, R. (2020). Hidden markov model approach for software reliability estimation with        logic        error. International        Journal        of        Automation        and        Computing, 17(2),        305–
  6. 320.    https://doi.org/10.1007/s11633-019-1214-7
  7. Chu, Y., & Xu, S. (2007). Exploration of complexity in software reliability. Tsinghua Science  and Technology, 12(S1), 266–269. https://doi.org/10.1016/s1007-0214(07)70122-0
  8. F. El-Sofany, H., A. Taj-Eddin, I., El-Hoimal, H., Al-Tourki, T., & Al-Sadoon, A. (2013). Enhancing software quality by an SPL testing based software testing. International Journal of Computer Applications, 69(6), 5– 13. https://doi.org/10.5120/11844-7574
  9. Foidl, H., & Felderer, M. (2016). Integrating software quality models into risk-based testing. Software Quality Journal, 26(2), 809–847. https://doi.org/10.1007/s11219-016-9345-3
  10. Helander, M. E., Ming Zhao & Ohlsson, N. (1998).  Planning  models  for  software  reliability  and cost. IEEE Transactions on Software Engineering, 24(6), 420–434. https://doi.org/10.1109/32.689400
  11. Janes, A., Scotto, M., Pedrycz, W., Russo, B., Stefanovic, M., & Succi, G. (2006). Identification of defect-prone classes in telecommunication software systems using design metrics. Information Sciences, 176(24), 3711–3734.    https://doi.org/10.1016/j.ins.2005.12.002
  12. Jones, C. B., & Randell, B. The role of structure: A dependability perspective. У Structure for dependability:   Computer-based   systems    from    an    interdisciplinary    perspective (с. 3–15).    Springer- Verlag.    https://doi.org/10.1007/1-84628-111-3_1
  13. Letichevsky, A., Kapitonova, J., Letichevsky, A., Volkov, V., Baranov, S., & Weigert, T. (2005). Basic protocols, message sequence charts, and the verification of requirements specifications. Computer Networks, 49(5), 661–675.    https://doi.org/10.1016/j.comnet.2005.05.005
  14. Long, Z. (2018). Research on the quality assurance method of spacecraft software based on software testing. Science Discovery, 6(1), 52. https://doi.org/10.11648/j.sd.20180601.19
  15. Malik, V., & Singh, S. (2017). Tools, strategies & models for incorporating software quality assurance in risk oriented testing. Oriental Journal of Computer Science and Technology, 10(3), 603–611. https://doi.org/10.13005/ojcst/10.03.08
  16. Nasar, M., Johri, P., & Chanda, U. (2014). Software testing resource allocation and release time problem: A review. International Journal of Modern Education and Computer Science, 6(2), 48–55. https://doi.org/10.5815/ijmecs.2014.02.7
  17. Pietrantuono, R. (2020). On the testing resource allocation problem: Research  trends  and perspectives. Journal of Systems and Software, 161, 110462. https://doi.org/10.1016/j.jss.2019.110462
  18. Sahu, K., & Srivastava, R. K. (2021). Predicting software bugs of newly and large datasets through a unified neuro-fuzzy approach: Reliability perspective. Advances in Mathematics: Scientific Journal, 10(1), 543– 555. https://doi.org/10.37418/amsj.10.1.54
  19. Samal, U., & Kumar, A. (2024). A neural network approach for software reliability prediction. International Journal of Reliability, Quality and Safety Engineering. https://doi.org/10.1142/s0218539324500098
  20. Shrivastava, A. K., Sharma, R., & Pham, H. (2022). Software reliability and cost models with warranty and life cycle. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X2210762.     https://doi.org/10.1177/1748006x221076273
  21. Tankala, D. K. (2023). Learning based Software Defect Prediction. International Journal of Computational Intelligence Research (IJCIR), 19(1), 51–62. https://doi.org/10.37622/ijcir/19.1.2023.51-62
  22. Upadhyay, R., & Johri, P. (2013). Review on Software Reliability Growth Models and Software Release Planning. International Journal of Computer Applications, 73(12), 1–7. https://doi.org/10.5120/12790-9769
  23. Yu, L., &  Mishra, A. (2012). Experience in Predicting  Fault-Prone Software Modules Using Complexity Metrics. Quality Technology & Quantitative Management, 9(4), 421–434. https://doi.org/10.1080/16843703.2012.11673302