SPECIFICS OF QUALITY ASSESSMENT MODELS APPLICATION AT DEVELOPMENT AND USE STAGES OF SOFTWARE SYSTEMS
DOI:
https://doi.org/10.31649/1999-9941-2024-59-1-127-138Keywords:
Capability maturity model integration, software quality assurance processes, agile testing quadrants, Six Sigma, total quality managemen, software process, group architecture frameworkAbstract
Abstract. The article examines the peculiarities of applying quality models at different stages of development and utilization of software systems to ensure their high quality. The main aim of the research is to identify new combined approaches for evaluating the effectiveness and impact of quality models usage at various stages of software product life cycles. The work employs a systematic approach to analysis, considering the interaction of quality models with different stages of software design, development, testing, and operation. Specifically, the role of quality models in enhancing the efficiency of development processes and their impact on the final product quality is discussed. The methodology involves analyzing existing quality models, adapting them to specific project conditions, and studying practical examples of quality model implementation in real projects. An analysis of existing quality models, their adaptation to specific conditions of software system projects, and an analysis of implementation in real projects are carried out. This approach has enabled us to obtain specific results and reveal key aspects of quality model implementation. The interaction of quality models with overall quality assurance strategies and their impact on improving the productivity and reliability of software systems is considered. In a general scientific aspect, the research boils down to evaluating the effectiveness of this approach and determining its key features. Further examination showed that the use of new combined quality models in the stages of design, development, and testing contributes to the improvement of various aspects of software quality. They not only define quality criteria but also ensure compliance with these criteria throughout the project life cycle. The obtained results confirm the importance of using combined quality models at all stages of software development. This contributes not only to the improvement of the final product quality but also to the efficiency of the entire development process. Our results can serve as a basis for the practical implementation of quality models in software projects and improving the overall quality level in this field.
References
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K. Sahu and R. K. Srivastava, "Predicting software bugs of newly and large datasets through a unified neuro-fuzzy approach: Reliability perspective," Advances in Mathematics: Scientific Journal, vol. 10, no. 1, pp. 543–555, 2021.
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J.-X. Chen, "Overall performance evaluation: new bounded DEA models against unreachability of ef-ficiency," The Journal of the Operational Research Society, vol. 65, no. 7, pp. 1120–1132, 2014.
M. Filz, C. Herrmann, and S. Thiede, "Simulation-based Assessment of Quality Inspection Strategies on Manufacturing Systems," Procedia CIRP, vol. 93, pp. 777–782, 2020.
A. Golabchi, S. Han, and S. AbouRizk, "A simulation and visualization-based framework of labor ef-ficiency and safety analysis for prevention through design and planning," Automation in Con-struction, vol. 96, pp. 310–323, 2018.
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References
Foidl, H., & Felderer, M. (2018). Integrating software quality models into risk-based testing. Software Quality Journal, 26(2018), 809–847.
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.
Sahu, K., Alzahrani, F. A., Srivastava, R. K., & Kumar, R. (2021). Evaluating the impact of prediction techniques: Software reliability perspective. Computers, Materials & Continua, 67(2), 1471–1488.
Sackey, S., Lee, D. E., & Kim, B. S. (2020). Duration Estimate at Completion: Improving Earned Value Management Forecasting Accuracy. KSCE Journal of Civil Engineering, 24(3), 693–702. https://doi.org/10.1007/s12205-020-0407-5
Sharma, P., & Sangal, A. L. (2020). Building and Testing a Fuzzy Linguistic Assessment Framework for Defect Prediction in ASD Environment Using Process-Based Software Metrics. Arabian Journal of Science and Engineering, 45(12), 10327–10351.
Hassouneh, Y., Turabieh, H., Thaher, T., Tumar, I., Chantar, H., & Too, J. (2021). Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction. IEEE Access, 9, 14239–14258. https://doi.org/10.1109/ACCESS.2021.3052149
Al-Qutaish, R. (2010). Quality Models in Software Engineering Literature: An Analytical and Comparative Study. Journal of American Science, 6, 10.
Estdale, J., & Georgiadou, E. (2018). Applying the ISO/IEC 25010 Quality Models to Software Product. In X. Larrucea, I. Santamaria, R. O'Connor, & R. Messnarz (Eds.), Systems, Software and Services Process Improvement. EuroSPI 2018. Communications in Computer and Information Science (Vol. 896, pp. 1-12).
Fonseca-Herrera, O., Rojas, A. E., & Florez, H. (2021). A Model of an Information Security Management System Based on NTC-ISO/IEC 27001 Standard. IAENG International Journal of Computer Science, 48, 213.
Aziz, M. N., Sapta, I. M., & Rochimah, S. (2018). Security Characteristic Evaluation Based on ISO/IEC 25023 Quality Model, Case Study: Laboratory Management Information System. In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) (pp. 332–336). IEEE.
Chen, J.-X. (2014). Overall performance evaluation: new bounded DEA models against unreachability of efficiency. The Journal of the Operational Research Society, 65(7), 1120–1132.
Filz, M., Herrmann, C., & Thiede, S. (2020). Simulation-based Assessment of Quality Inspection Strategies on Manufacturing Systems. Procedia CIRP, 93, 777–782.
Golabchi, A., Han, S., & AbouRizk, S. (2018). A simulation and visualization-based framework of labor efficiency and safety analysis for prevention through design and planning. Automation in Construction, 96, 310–323.
Han, P., Wang, L., & Song, P. (2016). Doubly robust and locally efficient estimation with missing outcomes. Statistica Sinica, 26(2), 691–719.
Hund, L., Schroeder, B., Rumsey, K., & Huerta, G. (2018). Distinguishing between model- and data-driven inferences for high reliability statistical predictions. Reliability Engineering and System Safety, 180, 201–210.
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