Information technology for prediction of software quality level

Tetiana Hovorushchenko, Yurii Voichur, Dmytro Medzatyi

Abstract


Today, there is a contradiction between the rapid increase in the complexity and size of modern software while increasing responsibility for the performance of their functions, the increasing requirements of customers and users to the quality and efficiency of software use and the imperfection of models, methods, tools of predicting software quality at the early stages of the life cycle.Therefore, the task of predicting the software quality level based on requirements is relevant. The aim of this study is to solve this task by developing information technology for prediction software quality levels based on requirements. The proposed information technology for prediction software quality level based on requirements provides analysis of quality attributes in requirements, reflects the dependence (equations) of quality characteristics on attributes, forms a quantitative assessment of quality characteristics, reflects the dependence (equation) of quality on its characteristics, forms a quantitative assessment of quality, performs quality level prediction, provides all the listed services simultaneously and the model, methods, and tools underlying the IT belong to common methodological approaches and are integrated. The developed system of predicting the software quality level based on requirements provides the user with predicted estimates of eight software quality characteristics, geometric interpretation of the software quality characteristics’ values, a comprehensive indicator of the predicted software quality, and a conclusion about the future software quality level. On the basis of this, it is possible to compare sets of requirements for software and make a reasonable choice of a set of requirements for further implementation. The information technology and the system of predicting the software quality level based on requirements, developed in this paper, provide the possibility of comparing sets of requirements for software, justified selection of requirements for further implementation of quality software (as experiments have shown, this is only one of the four proposed sets), and rejection or revision of unsuccessful sets of requirements that cannot be used to develop quality software.

Keywords


software quality; software quality characteristic; software quality attribute; set of software requirements; software quality level

Full Text:

PDF

References


Mahaju, S., Carver, J. C., & Bradshaw, G. L. Human error management in requirements engineering: Should we fix the people, the processes, or the environment? Information and Software Technology, 2023, vol. 160, article no. 107223. DOI: 10.1016/j.infsof.2023.107223.

Guerra-García, C., Nikiforova, A., Jiménez, S., Perez-Gonzalez, H. G., Ramírez-Torres, M., & Ontañon-García, L. ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Data quality by design. Data & Knowledge Engineering, 2023, vol. 145, article no. 102152. DOI: 10.1016/j.datak.2023.102152.

ISO/IEC 25010:2011. Systems and software engineering. Systems and software Quality Requirements and Evaluation (SQuaRE). System and software quality models. Geneva, 2011. 34 p.

Tamura, Y., & Yamada, S. Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software. International Journal of Mathematical, Engineering and Management Sciences, 2023, vol. 8, no. 4, pp. 632-639. DOI: 10.33889/ijmems.2023.8.4.036.

Bhandari, K., Kumar, K., & Sangal, A. L. Data quality issues in software fault prediction: a systematic literature review. Artificial Intelligence Review, 2023, vol. 56, pp. 7839-7908. DOI: 10.1007/s10462-022-10371-6.

Sadia, H., Abbas, S. Q., & Faisal, M. A Bayesian Network-Based Software Requirement Complexity Prediction Model. Lecture Notes on Data Engineering and Communications Technologies, 2022, vol. 139, pp. 197-213. DOI: 10.1007/978-981-19-3015-7_15.

Pulse of the Profession 2023: Power Skills, Redefining Project Success. 14th edition. Available at: https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pmi-pulse-of-the-profession-2023-report.pdf?v=7933da8f-304b-4fe3-a655-78dace54174a&rev=427949fcdb684485a020cc72ea219f32 (accessed 21.08.2023).

PMI’s Pulse of the Profession 9-th Global Project Management Survey. Available at: https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pulse/pulse-of-the-profession-2017.pdf (accessed 21.08.2023).

The Cost of Poor Software Quality in the US: A 2020 Report. Available at: https://www.it-cisq.org/pdf/CPSQ-2020-report.pdf (accessed 21.08.2023).

Babatunde, A. N., Ogundokum, R. O., Adeoye, L. B., & Misra, S. Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers. Mathematics, 2023, vol. 11, no. 12, article no. 2714. DOI: 10.3390/math11122714.

Hai, T., Zhou, J., Li, N., Jain, S. K., Agrawal, S., & Dhaou, I. B. Cloud-based bug tracking software defects analysis using deep learning. Journal of Cloud Computing, 2022, vol. 11, no. 1, article no. 32. DOI: 10.1186/s13677-022-00311-8.

Aversano, L., Bernardi, M. L., Cimitile, M., Iammarino, M., & Montano, D. Forecasting technical debt evolution in software systems: an empirical study. Frontiers of Computer Science, 2023, vol. 17, no. 3, article no. 173210. DOI: 10.1007/s11704-022-1541-7.

Alweshah, M., Kassaymeh, S., Alkhalaileh, S., Almseidin, M., & Altarawni, I. An Efficient Hybrid Mine Blast Algorithm for Tackling Software Fault Prediction Problem. Neural Processing Letters, 2023. DOI: 10.1007/s11063-023-11357-3.

Alghamidi, A., & Niazi, M. Toward Successful Secure Software Deployment: An Empirical Study. Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, Oulu, Finland, 2023, pp. 487-492. DOI: 10.1145/3593434.3593966.

Standish Group 2015 Chaos Report – Q&A with Jennifer Lynch. Available at: http://www.infoq.com/articles/standish-chaos-2015 (accessed 21.08.2023).

Li, J., & Liu, S. Requirements‐related fault prevention during the transformation from formal specifications to programs. IET Software, 2023, vol. 17, iss. 3, pp. 316-332. DOI: 10.1049/sfw2.12126.

Daun, M., Grubb, A. M., Stenkova, V., & Tenbergen, B. A systematic literature review of requirements engineering education. Requirements Engineering, 2023, vol. 28, pp. 145-175. DOI: 10.1007/s00766-022-00381-9.

Izonin, I., Kryvinska, N., Tkachenko, R., & Zub, K, An approach towards missing data recovery within IoT smart system. Procedia Computer Science, 2019, vol. 155, pp. 11-18. DOI: 10.1016/j.procs.2019.08.006.

Jadhav, A., Shandilya, S. K., Izonin, I., & Gregus, M. Effective Software Effort Estimation enabling Digital Transformation. IEEE Access, 2023, pp. 83523-83536. DOI: 10.1109/access.2023.3293432.

Huang, F., & Strigini, L. HEDF: A Method for Early Forecasting Software Defects based on Human Error Mechanisms. IEEE Access, 2023, pp. 3626-3652. DOI: 10.1109/access.2023.3234490.

Ahmad, K., Abdelrazek, M., Arora, C., Baniya, A. A., & Bano, M.; Grundy, J. Requirements engineering framework for human-centered artificial intelligence software systems. Applied Soft Computing, 2023, vol. 143, article no. 110455. DOI: 10.1016/j.asoc.2023.110455.

Zhi, Q., Gong, L., Ren, J., Liu, M., Zhou, Z., & Yamamoto, S. Element quality indicator: A quality assessment and defect detection method for software requirement specification. Heliyon, 2023, vol. 9, no. 5, article no. e16469. DOI: 10.1016/j.heliyon.2023.e16469.

ISO 25023:2016. Systems and software engineering. Systems and software Quality Requirements and Evaluation (SQuaRE). Measurement of system and software product quality. Geneva, 2016. 45 p.

Salomon, S., Duque, R., Montana, J. L., & Tenes, L. Towards automatic evaluation of the Quality-in-Use in context-aware software systems. Journal of Ambient Intelligence and Humanized Computing, 2023, vol. 14, pp. 10321-10346. DOI: 10.1007/s12652-021-03693-w.

Strielkina, A., & Tetskyi, A. Methodology for assessing satisfaction with requirements at the early stages of the software development process. Radioelectronic and Computer Systems, 2023, vol. 1, pp. 197–206. DOI: 10.32620/reks.2023.1.16.

Kononenko, I., Kpodjedo, M., Morhun, A., & Oliinyk, M. Information technology for choosing the project portfolio management approach and the optimal level of maturity of an organization. Radioelectronic and Computer Systems, 2022, vol. 4, pp. 173–190. DOI: 10.32620/reks.2022.4.14.

Kononenko, I., & Sushko, H. Mathematical model of software development project team composition optimization with fuzzy initial data. Radioelectronic and Computer Systems, 2021, vol. 3, pp. 149–159. DOI: 10.32620/reks.2021.3.12.

Hovorushchenko, T., Medzatyi, D., Voichur, Yu., & Lebiga, M. Method for forecasting the level of software quality based on quality attributes. Journal of Intelligent & Fuzzy Systems, 2023, vol. 44, no. 3, pp. 3891-3905. DOI: 10.3233/jifs-222394.

Hovorushchenko, T., & Pomorova, O. Methodology of Evaluating the Sufficiency of Information on Quality in the Software Requirements Specifications. Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, Kyiv, Ukraine, 2018, pp. 385-389. DOI: 10.1109/DESSERT.2018.8409161.

Hovorushchenko, T. Methodology of Evaluating the Sufficiency of Information for Software Quality Assessment According to ISO 25010. Journal of information and organizational sciences, 2018, vol. 42, no. 1, pp. 63–85. DOI: 10.31341/jios.42.1.4.

Hovorushchenko, T. Information Technology for Assurance of Veracity of Quality Information in the Software Requirements Specification. Advances in Intelligent Systems and Computing, 2018, vol. 689, pp. 166–185. DOI: 10.1007/978-3-319-70581-1_12.

Pomorova, O., & Hovorushchenko, T. Research of artificial neural network's component of software quality evaluation and prediction method. Proceedings of 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Prague, Czech Republic, 2011, vol. 2, pp. 959-962. DOI: 10.1109/IDAACS.2011.6072916.

Pomorova, O., & Hovorushchenko, T. Artificial neural network for software quality evaluation based on the metric analysis. Proceedings of 2012 10th East-West Design and Test Symposium, Kharkiv, Ukraine, 2012, pp. 200-203. DOI: 10.1109/EWDTS.2013.6673193.

Goyal, S. Comparison of Machine Learning Techniques for Software Quality Prediction. International Journal of Knowledge and Systems Science, 2020, vol. 11, no. 2, pp. 20-40. DOI: 10.4018/IJKSS.2020040102.

Masood, M., & Khan, M. Early Software Quality Prediction Based on Software Requirements Specification Using Fuzzy Inference System. Lecture Notes in Artificial Intelligence, 2018, vol. 10956, pp. 722-733. DOI: 10.1007/978-3-319-95957-3_75.

Arora, I., & Saha, A. Software fault prediction using firefly algorithm. International Journal of Intelligent Engineering Informatics, 2018, vol. 6, no. 3-4, pp. 356-377. DOI: 10.1504/IJIEI.2018.10013012.

Tomar, P., Mishra, R., & Sheoran, K. Prediction of quality using ANN based on Teaching-Learning Optimization in component-based software systems. Software-Practice & Experience, 2018, vol. 48, no. 4, pp. 896-910. DOI: 10.1002/spe.2562.

Tripathi, V., & Singh, M. An efficient metrics based self-adaptive design model by multiobjective gray wolf optimization with extreme learning machine for autonomic computing system application. Concurrency and Computation-Practice & Experience, 2022, vol. 34, no. 4, article no. e6609. DOI: 10.1002/cpe.6609.

Alshareet, O., Itradat, A., Abu Doush, I., & Quttoum, A. Incorporation of ISO 25010 with machine learning to develop a novel quality in use prediction system (QiUPS). International Journal of System Assurance Engineering and Management, 2018, vol. 9, no. 2, pp. 344-353. DOI: 10.1007/s13198-017-0649-x.

Sun, T., Lv, X., Cai, Y., Pan, Y., & Huang, J. Software test quality evaluation based on fuzzy mathematics. Journal of Intelligent & Fuzzy Systems, 2021, vol. 40, no. 4, pp. 6125-6135. DOI: 10.3233/JIFS-189451.

Lakra, K., & Chug, A. Application of metaheuristic techniques in software quality prediction: a systematic mapping study. International Journal of Intelligent Engineering Informatics, 2021, vol. 9, no. 4, pp. 355-399. DOI: 10.1504/IJIEI.2021.120322.

Padhy, N., Singh, R., & Satapathy, S. Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications. Cluster Computing - The Journal of Networks Software Tools and Applications, 2019, vol. 22, pp. 14559-14581. DOI: 10.1007/s10586-018-2359-9.

Khan, M., Elmitwally, N., Abbas, S., Aftab, S., Ahmad, M., Fayaz, M., & Khan, F. Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review. Scientific Programming, 2022, vol. 2022, article no. 2117339. DOI: 10.1155/2022/2117339.

Kaur, R., & Sharma, S. An ANN Based Approach for Software Fault Prediction Using Object Oriented Metrics. Communications in Computer and Information Science, 2019, vol. 955, pp. 341-354. DOI: 10.1007/978-981-13-3140-4_31.

Goyal, S. 3PcGE: 3-parent child-based genetic evolution for software defect prediction. Innovations in Systems and Software Engineering, 2023, vol. 19, pp. 197-216. DOI: 10.1007/s11334-021-00427-1.

Kumaresan, K., & Ganeshkumar, P. Software reliability modeling using increased failure interval with ANN. Cluster Computing - The Journal of Networks Software Tools and Applications, 2019, vol. 22, no. 2, pp. 3095-3102. DOI: 10.1007/s10586-018-1942-4.

Gordieiev, O., & Kharchenko, V. Profile-Oriented Assessment of Software Requirements Quality: Models, Metrics, Case Study. International Journal of Computing, 2020, vol. 19, no. 4, pp. 656–665. DOI: 10.47839/ijc.19.4.2001.

Gordieiev, O., Kharchenko, V., & Gordieieva, D. Software Requirements Profile Quality Model. International Journal of Computing, 2022, vol. 21, no. 1, pp. 111–119. DOI: 10.47839/ijc.21.1.2524.




DOI: https://doi.org/10.32620/reks.2023.3.19

Refbacks

  • There are currently no refbacks.