Implementation of artificial intelligence methods to the processes of automated metrics forecasting for software systems development projects
DOI:
https://doi.org/10.30837/2522-9818.2024.2.153Keywords:
project evaluation; software; machine learning; generative models.Abstract
The subject matter of the article is the process of automated forecasting of project metrics for software development projects that are typically subject to evaluation. It also covers AI methods and models that can be used to generate basic roadmap templates and operational work lists, as well as alternative estimates depending on the context. The goal of the work is to study the foundations of creating a system for automated predicting of alternative evaluations of a software product. The following tasks were solved in the article: determining the stages of evaluation related to the assessment of alternatives in the life cycle of a software development project; investigating the problems of predicting and the main factors affecting the final indicators; exploring predicting methods that can be used to implement multivariate assessment of a software development project. The following methods are used: methods for evaluating and predicting labor costs in software development projects, machine and deep learning, and assessing their effectiveness for solving the prediction problem. The following results were obtained: the conceptual foundations for creating automated evaluation and prediction systems based on the analysis of the effectiveness of selected machine learning models were determined, the areas of application for artificial intelligence methods in the process of evaluating software development project indicators were identified, the performance indicators of various machine learning models were assessed based on certain model evaluation parameters that characterize prediction accuracy; a conceptual architecture of a project roadmap generation software tool based on the GPT language model was proposed. Conclusions: the use of machine and deep learning methods can improve the accuracy of predictions for key project indicators, provide the possibility of flexible generation of various alternative roadmap templates and operational work lists, making the planning and management process more efficient and transparent under conditions of high uncertainty of project requirements.
References
Список літератури
Lauesen, S. IT project failures, causes and cures. IEEE Access. 2020. Vol. 8. P. 72059–72067. DOI: https://doi.org/10.1109/ACCESS.2020.2986545
SaasList. The State of Project Management in 2023 [42 Statistics]. 2023. URL: https://saaslist.com/blog/project-management-statistics/ (дата звернення: 15.04.2023).
Gupta R. G., Dumka A., Mazumdar B. D. Software Cost Estimation: A Comparative Analysis. 2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE). 2024. P. 1–8. DOI: https://doi.org/10.1109/ICCECE58645.2024.10497286
Nesma. What is Function Point Analysis (FPA) and what are function points? 2015. URL: https://nesma.org/faq/function-point-analysis-fpa-function-points/ (дата звернення: 17.04.2024).
Brar P., Nandal D. A Systematic Literature Review of Machine Learning Techniques for Software Effort Estimation Models. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT). 2022. P. 494–499. DOI: https://doi.org/10.1109/CCiCT56684.2022.00093
Milošević D. Project Management ToolBox. Tools and Techniques for the Practicing Project Manager. Wiley, Hoboken, New Jersey, 2003. ISBN: 9780471208228. 584 p.
Wolverton R. W. The Cost of Developing Large-Scale Software. IEEE Transactions on Computers. 1974. Vol. C-23. No. 6. P. 615–636. DOI: https://doi.org/10.1109/T-C.1974.224002
APMP. Competitive Price To Win. 2023. URL: https://www.apmp.org/assets/BoK-PTW-M-v4.pdf (дата звернення: 19.04.2024).
Affenzeller M., Wagner S., Winkler S., Beham A. Genetic Algorithms and Genetic Programming. Modern Concepts and Practical Applications. CRC Press, Boca Raton, Florida. 2009. ISBN: 9781420011326. 379 p.
Kim A., Lee D. Dynamic Bayesian network-based situational awareness and course of action decision-making support model. Expert Systems with Applications. 2024. Vol. 252, Part A. 124093 р. DOI: https://doi.org/10.1016/j.eswa.2024.124093
Chong L. W., Rengasamy D., Wong Y. W., Rajkumar R. K. Load prediction using support vector regression. TENCON 2017 – 2017 IEEE Region 10 Conference. 2017. P. 1069–1074. DOI: https://doi.org/10.1109/TENCON.2017.8228016
Elish, M. O. Improved estimation of software project effort using multiple additive regression trees. Expert Systems with Applications. 2009. Vol. 36, No. 7. P. 10774–10778. DOI: https://doi.org/10.1016/j.eswa.2009.02.013
Yunning Z., Xixi S. Research on Improved PERT Model in Analysis of Schedule Risk of Project. 2010 International Conference on E-Business and E-Government. 2010. P. 2768-2771. DOI: https://doi.org/10.1109/ICEE.2010.699
Cunnama L. (nee Shillington), Sinanovic E., Ramma L., Foster N., Berrie L., Stevens W., Molapo S., Marokane P., McCarthy K., Churchyard G., Vassall A. Using Top-down and Bottom-up Costing Approaches in LMICs: The Case for Using Both to Assess the Incremental Costs of New Technologies at Scale. Health economics. 2016. Vol. 25. P. 53–66. DOI: https://doi.org/10.1002/hec.3295
Biletskiy, Y., Campeanu, C., Dudar, Z., Vorochek, O. Meta-context mediation to attain semantic interoperability. 2004 2nd International IEEE Conference on 'Intelligent Systems'. 2004. Vol. 1. P. 238–243. DOI: https://doi.org/10.1109/IS.2004.1344674
References
Lauesen, S. (2020), "IT project failures, causes and cures", IEEE Access, Vol. 8, P. 72059–72067. DOI: https://doi.org/10.1109/ACCESS.2020.2986545
SaasList (2023), "The State of Project Management in 2023 [42 Statistics]", available at: https://saaslist.com/blog/project-management-statistics/ (last accessed 15.04.2023).
Gupta, R. G., Dumka, A., Mazumdar, B. D. (2024), "Software Cost Estimation: A Comparative Analysis", 2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE), Р. 1–8, DOI: https://doi.org/10.1109/ICCECE58645.2024.10497286.
Nesma (2023), "What is Function Point Analysis (FPA) and what are function points?" available at: https://nesma.org/faq/function-point-analysis-fpa-function-points/ (last accessed 17.04.2024).
Brar, P., Nandal, D. (2022), "A Systematic Literature Review of Machine Learning Techniques for Software Effort Estimation Models", 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT), Р. 494–499, DOI: https://doi.org/10.1109/CCiCT56684.2022.00093
Milošević, D. (2003), Project Management ToolBox. Tools and Techniques for the Practicing Project Manager, Wiley, Hoboken, New Jersey, 584 p. ISBN: 9780471208228
Wolverton, R. W. (1974), "The Cost of Developing Large-Scale Software", IEEE Transactions on Computers, Vol. C-23, No. 6, P. 615–636. DOI: https://doi.org/10.1109/T-C.1974.224002
APMP (2023), "Competitive Price To Win", available at: https://www.apmp.org/assets/BoK-PTW-M-v4.pdf (last accessed 19.04.2024).
Affenzeller, M., Wagner, S., Winkler, S., Beham, A. (2009), Genetic Algorithms and Genetic Programming. Modern Concepts and Practical Applications, CRC Press, Boca Raton, Florida, 379 p. ISBN: 9781420011326.
Kim, A., Lee, D. (2024), "Dynamic Bayesian network-based situational awareness and course of action decision-making support model", Expert Systems with Applications, Vol. 252, Part A. 124093 р., DOI: https://doi.org/10.1016/j.eswa.2024.124093.
Chong, L. W., Rengasamy, D., Wong, Y. W., Rajkumar, R. K. (2017), "Load prediction using support vector regression", TENCON 2017 – 2017 IEEE Region 10 Conference, 1069–1074 р. DOI: https://doi.org/10.1109/TENCON.2017.8228016
Elish, M. O. (2009), "Improved estimation of software project effort using multiple additive regression trees", Expert Systems with Applications, Vol. 36, No. 7, Р. 10774–10778, DOI: https://doi.org/10.1016/j.eswa.2009.02.013
Yunning, Z., Xixi, S. (2010), "Research on Improved PERT Model in Analysis of Schedule Risk of Project", 2010 International Conference on E-Business and E-Government, Р. 2768–2771. DOI: https://doi.org/10.1109/ICEE.2010.699
Cunnama, L. (nee Shillington), Sinanovic, E., Ramma, L., Foster, N., Berrie, L., Stevens, W., Molapo, S., Marokane, P., McCarthy, K., Churchyard, G., Vassall, A. (2016), "Using Top-down and Bottom-up Costing Approaches in LMICs: The Case for Using Both to Assess the Incremental Costs of New Technologies at Scale", Health economics, Vol. 25, Р. 53–66. DOI: https://doi.org/10.1002/hec.3295
Biletskiy, Y., Campeanu, C., Dudar, Z., Vorochek, O. (2004), "Meta-context mediation to attain semantic interoperability", 2004 2nd International IEEE Conference on Intelligent Systems, Vol. 1, Р. 238–243, DOI: https://doi.org/10.1101/IS.2004.1344674
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