Software development life cycle duration assessment system based on intelligent information processing

The article presents a system for assessing the durability of the software development life cycle based on the use of artificial intelligence technologies. An analysis of existing approaches to the science of labor costs and development times is presented, based on which the choice of neural network technologies is substantiated as the most promising direction for solving forecasting problems under uncertainty. The main groups of factors influencing the duration of the development process are identified and classified: technical, organizational, team, historical, resource, external. Based on the classes of factors, constant distribution of input parameters, application for training neural networks, as well as their hyperparameters. The architectural characteristics of neural networks, the number of layers, types of activation functions, optimization methods and control parameters studied in the experiments are given. An algorithm for assessing the timing has been developed, implemented as a software system that provides operational forecasting of the durability of project development based on the analysis of historical data and current project analytics. An example of assessing the development times using the developed system is given and the results are compared with an expert assessment. The proposed system for analyzing the duration of the reduction and increasing the accuracy of the estimate in comparison with the reduction methods.

1. Shulga T.E., Khramov D.E. Software System Architecture for Estimating Software Development Time. Software Engineering. 2024;15(9):476–484. (In Russ.). https://doi.org/10.17587/prin.15.476-484

2. Zhang W., Mouhamad I., Saklakov V.M., Jayakody D.N.K. Neural Network to Optimize the Adaptive Exponential Min Sum Decoding Algorithm. Modeling, Optimization and Information Technology. 2025;13(1). https://doi.org/10.26102/2310-6018/2025.48.1.026

3. Gu Chongyu, Gromov M.L. Artificial Neural Network for Image Blending Artifact Suppression in Differential Activation-Based Face Attribute Editing. Modeling, Optimization and Information Technology. 2025;13(3). (In Russ.). https://doi.org/10.26102/2310-6018/2025.50.3.013

4. Oba K.M. Development of a Scheffe's Model to Predict the Durations of Project Tasks. Journal of Engineering Research and Reports. 2024;26(1):117–124. https://doi.org/10.9734/JERR/2024/v26i11067

5. Garcia-Diaz N., Garcia-Virgen Ju., Farias-Mendoza N., et al. Software Development Time Estimation Based on a New Neuro-Fuzzy Approach. In: 2015 10th Iberian Conference on Information Systems and Technologies (CISTI), 17–20 June 2015, Aveiro, Portugal. IEEE; 2015. P. 1–7. https://doi.org/10.1109/CISTI.2015.7170378

6. López-Martín С., Abran A. Neural Networks for Predicting the Duration of New Software Projects. Journal of Systems and Software. 2015;101:127–135. https://doi.org/10.1016/j.jss.2014.12.002

7. Singal P., Kumari A.Ch., Sharma P. Estimation of Software Development Effort: A Differential Evolution Approach. Procedia Computer Science. 2020;167:2643–2652. https://doi.org/10.1016/j.procs.2020.03.343

8. Hamada M.A., Abdallah A., Kasem M., Abokhalil M. Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects. In: 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), 28–30 April 2021, Nur-Sultan, Kazakhstan. IEEE; 2021. P. 1–7. https://doi.org/10.1109/SIST50301.2021.9465887

9. Bundschuh M., Dekkers C. The IT Measurement Compendium: Estimating and Benchmarking Success with Functional Size Measurement. Berlin, Heidelberg: Springer; 2008. 644 p. https://doi.org/10.1007/978-3-540-68188-5

10. Khramov D.E. Normalizatsiya raznorodnykh naborov dannykh dlya prognozirovaniya srokov razrabotki programmnogo obespecheniya. In: Problemy upravleniya v sotsial'no-ekonomicheskikh i tekhnicheskikh sistemakh: Materialy XXI Mezhdunarodnoi nauchno-prakticheskoi konferentsii, 17–18 April 2025, Saratov, Russia. Saratov: Izdatel'skii tsentr "Nauka"; 2025. P. 202–206. (In Russ.).