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An effective approach for software project effort and duration estimation with machine learning algorithms
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“While there are some examples of using ML algorithms for project management (two examples in ML for SE by Nassif et al. (2016) and Pospieszny et al. (2018)), our primary study pool does not include any practices for managing ML systems development projects.”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Pospieszny et al., 2018
Citations
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Cited by
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Items 201–247 of 247
Estimación del esfuerzo de proyectos de software con algoritmos de aprendizaje de máquinas
de México, U. N. A., Martínez, J. I. S., González, M. G. E. I., de México, U. N. A., Fuentes-Pineda, G., & de México, U. N. A. (2019) · RECIBE REVISTA ELECTRÓNICA DE COMPUTACIÓN INFORMÁTICA BIOMÉDICA Y ELECTRÓNICA · doi:10.32870/recibe.v8i1.134 · Cited by 1
Predicting Group Size for Software Issues in an Open-Source Software Development Environment
Chopra, D., & Kaur, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5148-2_21 · Cited by 1
Semantic-based false alarm detection approach via machine learning
Qian, M., Luo, J., Ge, Y., Sun, C. K., Ge, X., & Huang, W. (2021) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c55045.2021.00019 · Cited by 1
Polish Contribution to Software Engineering Research 1992–2021
Szydłowska, J., & Swacha, J. (2023) · Foundations of Computing and Decision Sciences · doi:10.2478/fcds-2023-0005 · Cited by 1
Balakrishna, S. (2023) · Digital Policy Regulation and Governance · doi:10.1108/dprg-11-2021-0147 · Cited by 1
Software Development Effort Estimation Using Relational Database and Optimized Learning Mechanism
N, R. K. B., & Suresh, Y. (2023) · Journal of Computer Science · doi:10.3844/jcssp.2023.540.553 · Cited by 1
“ML approaches are developed instead of purely mathematical or statistical calculations as in other approaches (Pospieszny et al ., 2018).”
MethodZohrehvandi, S., Soltani, R., Lefebvre, D., Zohrehvandi, M., & Tenera, A. M. B. R. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40395-8_6 · Cited by 1
Gora, R. K., & Sinha, R. R. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10s.7624 · Cited by 1
G, P. V. A., & Kumari, K. A. (2023) · Research Square · doi:10.21203/rs.3.rs-2077635/v1 · Cited by 1
Hacia una metodologia para el diseño de contratos inteligentes
Iztapalapa, U. A. M. U., Solis, C. O., Pérez-Cortés, E., Iztapalapa, U. A. M. U., Maceda, H. C., & Iztapalapa, U. A. M. U. (2019) · RECIBE REVISTA ELECTRÓNICA DE COMPUTACIÓN INFORMÁTICA BIOMÉDICA Y ELECTRÓNICA · doi:10.32870/recibe.v8i1.132
Regression Model to Estimate Effort for Software-Oriented Projects
Ananth, V. V., Aditya, S., Kesavan, S. R., & Keerthiga, B. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-8289-9_5
Проекты по разработке программного обеспечения: процесс сбора и разметки данных
Buravov, A. A., & Mohamed, A. H. (2022) · INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES · doi:10.54309/ijict.2020.1.1.058
Error Reduction in Long-Term Mine Planning Estimates Using Deep Learning ModelsPreprint
Aguilera, C. O. D., Campos, P. G., & Risso, N. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4167815
Software Effort and Duration Estimation Using SVM and Logistic Regression
Gantayat, S. S., & Aditya, V. J. (2022) · Artificial intelligence-enhanced software and systems engineering · doi:10.1007/978-3-031-11713-8_28
Finding Significant Project Issues with Machine Learning
Vajjhala, N. R., & Strang, K. D. (2023) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-15175-0_2
Can Artificial Intelligence Learn to be a Project Professional?
Wang, K., & Stewart, I. N. (2022) · doi:10.61175/tema4085
de Sousa, W. T., Miranda, R. D. C., & Montevechi, J. A. B. (2018) · Anais do Simpósio Brasileiro de Pesquisa Operacional · doi:10.59254/sbpo-2018-85540
Patchala, S., Adnan, M. M., Vontela, T., Chanti, Y., & Indira., R. (2023) · International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) · doi:10.1109/aikiie60097.2023.10390074
“In [15], Pospieszny, Czarnacka-Chrobot, and Kobylinski created an ensemble of three machine learning models, including Generalised Linear Models (GLM), SVM, and NN.”
MethodA Predictive Model for Software Cost Estimation Using ARIMA Algorithm
Draz, M. M., Emam, O., & Azzam, S. M. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150764
“Posbiezny et al. [21] built a model using neural networks, support vector machines with cross-validation, and generalized linear models in which the described set of algorithms was averaged using the ISBSG datasets.”
MethodForecasting software indicators: an industry-academia collaboration
Ayala, C. P., Gómez, C., Manzano, M., Abhervé, A., & Franch, X. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10508-x
“(Pospieszny et al. 2018), an ensemble averaging of three machine learning algorithms are proposed for forecasting software project effort and duration.”
Jaiswal, A., Raikwal, J., & Raikwal, P. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70789-6_23
МЕТОД МАШИННОГО НАВЧАННЯ В УПРАВЛІННІ ПРОГРАМНИМИ ПРОЕКТАМИ
Бедратюк, Г. І. (2024) · Measuring and computing devices in technological processes · doi:10.31891/2219-9365-2024-80-3
AI in Software Effort Estimation
Ranković, N., Ranković, D., Ivanović, M. D., & Lazić, L. (2024) · Artificial intelligence-enhanced software and systems engineering · doi:10.1007/978-3-031-76572-8_4
A Deep Learning-Based Technique for Evaluation of Estimation in Software Development
Kumar, B., Sundari, M. R., & SUREKHA, A. V. L. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6995-7_22
Atiyaa, M. A., & Al-jawaherry, M. A. (2025) · AIP conference proceedings · doi:10.1063/5.0259373
Kaushik, A., Yadav, K., Kaur, P., Sheoran, K., Bhutani, N., Kapur, R., & Singh, B. (2025) · Artificial intelligence-enhanced software and systems engineering · doi:10.1007/978-3-031-88188-6_6
Agile Effort Estimation Usage in the Sri Lankan Software Industry
Lee, S. J., & Senapathi, M. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-94544-1_14
Software Cost Estimation for using Neuro-Fuzzy Agile Hybrid Approach
Kumar, S., & Singh, M. P. (2025) · WSEAS TRANSACTIONS on SYSTEMS archive · doi:10.37394/23202.2025.24.44
Nhung, H. L. T. K., Šilhavý, P., & Šilhavý, R. (2025) · IEEE Access · doi:10.1109/access.2025.3586081
“These characteristics are particularly valuable in software, offering essential insights into a project’s nature that are often overlooked [17], [31], [59], [60].”
A Review of Estimation in Software Engineering
Tomescu, K. J., Gowran, N., Gomez, L., Delahunty, E., McCarren, A. L., Marks, G., Yılmaz, M., Messnarz, R., & Clarke, P. M. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-04288-0_10
Reducing Project Uncertainty through Data-Driven Management: A Bibliometric Analysis
Salykova, L., & Zhandos, M. (2026) · The economy strategy and practice · doi:10.51176/1997-9967-2025-4-97-113
Expert-Integrated Stacking Model for Estimating Software Development Effort
Haris, M., Chua, F.-F., & Lim, A. H.-L. (2025) · IEEE International Conference on Computing (ICOCO) · doi:10.1109/icoco67189.2025.11334137
“Consequently, ML techniques have recently acquired prominence among researchers for improving SDEE and overcoming the inherent limitations of traditional techniques [5], [11], [12], [13], [14], [15].”
InfluentialOptimizing Risk Integrated Software Effort Estimation: A Differential Evolution Approach
Singal, P., SHARMA, P., & Kumari, A. C. (2026) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-95-3596-5_18
Advanced effort estimation in software projects using ensemble-based fusion techniques
Ritu, Bhambri, P., Khan, M., & Mujahid, B. M. (2026) · Egyptian Informatics Journal · doi:10.1016/j.eij.2026.100914
Malik, V., Arora, R., & Singh, H. (2025) · International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN) · doi:10.1109/emergin67762.2025.11450870
“Over the last two decades, machine learning has emerged as a powerful approach for software effort estimation, offering alternatives to parametric and algorithmic models [3].”
Locality‐Based Self‐Adjustable Ensemble Approach for Effort Estimation
Shukla, S., & Kumar, S. M. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70109
User Story Estimation using Natural Language Processing and Deep Learning: A Comparative Study
Silva, D. D. O., Souza, A. C. C., Souza, F. C. M., & Sanches, S. R. R. (2026) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2026.5860
Morgan, S. M., & Daniels, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6864774
Azimi, S., Hakiminejad, Y., Shevade, D. J., & Tavakoli, A. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.111444 · Cited by 2
A Predict-then-Optimize Job Assignment Framework for Efficient Trademark Application Evaluation
Khaniyev, T., Özdemir, B., Işık, E. S., Yöner, E. R., & Köse, B. E. (2026) · Journal of Turkish Operations Management · doi:10.56554/jtom.1838809
Software design phase risk estimation using extreme learning machine
Khan, T., Islam, M., & Khan, W. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-09258-w
“Machine learning methods have been used to predict and assess software development risks and mitigate them throughout the software development cycle [15].”
Murzagaleev, T. M., & Zhukova, N. A. (2026) · IEEE International Conference on Soft Computing and Measurements (SCM) · doi:10.1109/scm71573.2026.11625887
“Tables 4–6 present the metrics obtained from applying predictive models to the test datasets for road projects, site preparation projects, and the combined project, with effort estimations from the road department, general planning department, and land management department, respectively.”
López‐Martín, C., Yáñez-Márquéz, C., & Nassif, A. B. (2026) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2026.p1417
Gahlaut, A., & Kaushik, A. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70175
Ostovari, A., Rezaali, M., Faghihi, S. V., & Nourzad, S. H. H. (2026) · Iranian Journal of Science and Technology Transactions of Civil Engineering · doi:10.1007/s40996-026-02254-5
Metadata
- Authors
- Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A.
- Affiliations
- SGH Warsaw School of Economics (PL)
- Venue
- Journal of Systems and Software
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 137
- Pages
- 184-196
- ISSN
- 0164-1212 (ISSN-L)
- OpenAlex
- W2770963638
- Cited by
- 247
- References
- 89
- Citations by year
- 2026: 15 · 2025: 25 · 2024: 36 · 2023: 42 · 2022: 42 · 2021: 37 · 2020: 28 · 2019: 19 · 2018: 3
- FWCI
- 25.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Systems and Software (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Reliability and Analysis Research
- Keywords
- software effort estimation, machine learning deployment, ISBSG dataset, ensemble averaging, support vector machine, artificial neural networks, generalized linear models, decision support system, software project management
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