Use of methods and tools for ensuring software quality
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2025.3.05Keywords:
comprehensive model, quality standards, integration testing, unit testing, technological challenges, continuous integrationAbstract
This paper proposes an examination of effective methods and tools for ensuring software quality. The scope of this topic includes current issues related to software quality assurance within the context of analyzing methods and tools used in practice to develop high-quality software. During the modeling process, a new comprehensive model for software quality assurance has been developed, combining modular testing, integration testing, and continuous integration methods. The advantage of this development is its enhanced adaptability to addressing key challenges in software quality assurance. Based on the developed model, strategies and approaches are proposed to improve configuration management processes and identify vulnerabilities in software systems.
References
H. Foidl, M. Felderer, “Integrating software quality models into risk-based testing,” Software Quality Journal, vol. 26, no. 2, pp. 809–847, 2016. doi: https://doi.org/10.1007/s11219-016-9345-3
K. Sahu, 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. doi: https://doi.org/10.37418/amsj.10.1.54
K. Sahu, Fahad A. Alzahrani, R.K. Srivastava, R. Kumar, “Evaluating the Impact of Prediction Techniques: Software Reliability Perspective,” Computers, Materials & Continua, vol. 67, no. 2, pp. 1471–1488, 2021. doi: https://doi.org/10.32604/cmc.2021.014868
S. Sackey, D.-E. Lee, B.-S. Kim, “Duration Estimate at Completion: Improving Earned Value Management Forecasting Accuracy,” KSCE Journal of Civil Engineering, vol. 24, issue 3, pp. 693–702, 2020. doi: https://doi.org/10.1007/s12205-020-0407-5
P. Sharma, A.L. Sangal, “Building and Testing a Fuzzy Linguistic Assessment Framework for Defect Prediction in ASD Environment Using Process-Based Software Metrics,” Arabian Journal for Science and Engineering, vol. 45, issue 12, pp. 10327–10351, 2020. doi: https://doi.org/10.1007/s13369-020-04701-5
Y. Hassouneh et al., “Boosted Whale Optimization Algorithm with Natural Selection Operators for Software Fault Prediction,” IEEE Access, vol. 9, pp. 14239–14258, 2021. doi: https://doi.org/10.1109/access.2021.3052149
Rafa Al-Qutaish, “Quality Models in Software Engineering Literature: An Analytical and Comparative Study,” Journal of American Science, vol. 6, pp. 166–175, 2010.
J. Estdale, E. Georgiadou, “Applying the ISO/IEC 25010 Quality Models to Software Product,” in Larrucea X., Santamaria I., O’Connor R., Messnarz R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2018. Communications in Computer and Information Science, vol. 896, 12 p., 2018. Springer, Cham. doi: https://doi.org/10.1007/978-3-319-97925-0_42
C. Zhang B. Li, L. Wang, H. Xu, T. Shao, “A Hierarchical Model for Quality Evaluation of Mixed Source Software Based on ISO/IEC 25010,” International Journal of Software Engineering and Knowledge Engineering, 33(02), pp. 1–25, 2022. doi: https://doi.org/10.1142/S021819402250070X
Elder Bruno Evaristo Correa, Jeffson Celeiro Sousa, Antônio Jorge Gomes Abelem, Sandro Ronaldo Bezerra Oliveira, “An evaluation of Security Features based on Iso/Iec 25023 for a Distributed Autonomic Scientific Publisher Tool on a Permissioned Blockchain,” Journal of Information Systems and Technology Management, vol. 19, 2022. doi: https://doi.org/10.4301/S1807-1775202219020
J.-X. Chen, “Overall performance evaluation: new bounded DEA models against unreachability of efficiency,” Journal of the Operational Research Society, vol. 65, issue 7, pp. 1120–1132, 2014. doi: https://doi.org/10.1057/jors.2013.57
M.-A. Filz, C. Herrmann, S. Thiede, “Simulation-based Assessment of Quality Inspection Strategies on Manufacturing Systems,” Procedia CIRP, vol. 93, pp. 777–782, 2020. doi: https://doi.org/10.1016/j.procir.2020.04.069
A. Golabchi, S. Han, S. AbouRizk, “A simulation and visualization-based framework of labor efficiency and safety analysis for prevention through design and planning,” Automation in Construction, vol. 96, pp. 310–323, 2018. doi: https://doi.org/10.1016/j.autcon.2018.10.001
P. Han, L. Wang, P.X.K. Song, “Doubly robust and locally efficient estimation with missing outcomes,” Statistica Sinica, 26(2), 2016. doi: https://doi.org/10.5705/ss.2014.030
L. Hund et al., “Distinguishing between model- and data-driven inferences for high reliability statistical predictions,” Reliability Engineering & System Safety, vol. 180, pp. 201–210, 2018. doi: https://doi.org/10.1016/j.ress.2018.07.017
