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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
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Items 101–200 of 247
Self-Adaptive Ensemble-based Approach for Software Effort Estimation
Shukla, S., & Kumar, S. (2023) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner56733.2023.00060 · Cited by 10
“Pospieszny et al. [29] also developed heterogeneous ensemble models combining MLP, Generalized Linear Model, and Support Vector Machine and showed improved results.”
ResultLi, J., Sun, S., Li, X., Chen, Z., & He, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-67197-1 · Cited by 8
Exploring stacking methods for software effort estimation with hyperparameter tuning
Hassanali, M., Soltanaghaei, M., Gandomani, T. J., & Boroujeni, F. Z. (2025) · Cluster Computing · doi:10.1007/s10586-024-04876-8 · Cited by 8
“Other datasets, such as the Promise software engineering repository or the COCOMO dataset, are obsolete or unreliable [9].”
Mapping the AI Landscape in Project Management Context: A Systematic Literature Review
Khalil, M., Bravo, A., Vieira, D. R., & de Carvalho, M. M. (2025) · Systems · doi:10.3390/systems13100913 · Cited by 8
“These challenges include navigating cost constraints, adhering to timelines, and maintaining quality standards under conditions of uncertainty and limited early-stage information, which together constrain accurate forecasting and decision-making [2].”
InfluentialA Systematic Literature Review of Machine Learning Applications in Software Engineering
Mezouar, H., & Afia, A. E. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-07969-6_24 · Cited by 8
Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods
Strang, K. D., & Vajjhala, N. R. (2023) · International Journal of Information Technology Project Management · doi:10.4018/ijitpm.317221 · Cited by 8
Optimizing Software Effort Estimation Models Based On Metaheuristic Methods: A Proposed Framework
Universitas Amikom Yogyakarta, I., & Marco, R. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/5181.52019 · Cited by 8
Design and Development of Machine Learning Technique for Software Project Risk Assessment - A Review
Mahdi, M. N., H, M. Z. M., Yusof, A., Cheng, L. K., Azmi, M. S. M., & Ahmad, A. R. (2020) · International Conference on Information Technology and Multimedia (ICIMU) · doi:10.1109/icimu49871.2020.9243459 · Cited by 8
“Review by Pospieszny, et al. [21] is intended to overcome these shortcomings and close a void in the original project development initiative life cycle between up-to-date testing results and the future application in the operation of reliable ML algae.”
BackgroundPrediction of Software Effort in the Early Stage of Software Development: A Hybrid Model
Rai, P., Kumar, S., & Verma, D. K. (2021) · Canadian Journal of Electrical and Computer Engineering · doi:10.1109/icjece.2021.3084850 · Cited by 8
Software Analysis Method for Assessing Software Sustainability
Saputri, T. R. D., & Lee, S.-W. (2020) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194020500047 · Cited by 8
Hariyanti, E., Janeswari, M. B., Moningka, M. M., Aziz, F. M., Putri, A. R., Hapsari, O. S., Sutha, N. A. A. D., Sinaga, Y. A. A., & Bendesa, M. P. (2023) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.9.2.305-319 · Cited by 7
“Reference [57] created a machine learning model using an ensemble method of three machine learning algorithms (Support Vector Machines, Neural Networks and Generalized Linear Models) to estimate effort and duration intended to provide a decision support tool for organizations that develop or…”
MethodMachine learning in project schedule creation: a systematic literature review
Koszykowski, M., & Orzeszko, W. (2025) · Journal of Scheduling · doi:10.1007/s10951-025-00857-w · Cited by 7
Multi-Step Dynamic Ensemble Selection to Estimate Software Effort
Jadhav, A., Shandilya, S. K., Izonin, I., & Muzyka, R. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2351718 · Cited by 7
“Pospieszny, Czarnacka-Chrobot, and Kobylinski (2018), proposed three machine learning models; SVM, MLP and GLM, and ensembled them to obtain an average of the three models.”
Resilient Project Scheduling Using Artificial Intelligence: A Conceptual Framework
Dahmani, S., Ben-Ammar, O., & Jebali, A. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-85874-2_33 · Cited by 8
Agile Processes in Software Engineering and Extreme Programming
Stray, V. G., Stol, K.-J., Paasivaara, M., & Kruchten, P. B. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08169-9 · Cited by 9
An Ensemble-Based Framework to Estimate Software Project Effort
Haris, M., Chua, F.-F., & Lim, A. H.-L. (2023) · International Conference on Software Engineering and Computer Systems · doi:10.1109/icsecs58457.2023.10256337 · Cited by 6
Feature importance for software development effort estimation using multi level ensemble approaches
Rao, K. E., Terlapu, P. V., Naidu, P. A., Tammineni, R. K., & Pydi, B. M. (2024) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v13i2.5531 · Cited by 6
G, P. V. A., & Kumari, K. A. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11010-9 · Cited by 6
“Generalized Linear Model (GLM) [13] is a statistical model that evaluates parameters using maximum likelihood estimation.”
Khatri, S., & Bansal, P. (2024) · IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) · doi:10.1109/iciteics61368.2024.10625522 · Cited by 6
“The project effort estimation literature has, however, put forth a number of prediction models, mostly for software development projects [2].”
A Hybrid Metaheuristic Aware Enhanced Deep Learning Approach for Software Effort Estimation
Bbadana, M., & Kiran, M. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8890 · Cited by 6
Resmi, V., & Vijayalakshmi, S. (2019) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126620500917 · Cited by 6
Using Machine Learning Technique for Effort Estimation in Software Development
Corrêa, W. A., Rivero, L., Bráz, G., & Viana, D. (2019) · Brazilian Symposium on Software Quality (SBQS) · doi:10.1145/3364641.3364670 · Cited by 6
“[29] Przemyslaw Pospieszny, Beata Czarnacka-Chrobot, and Andrzej Kobylinski.”
BackgroundSoftware reusability metrics prediction and cost estimation by using machine learning algorithms
Panigrahi, R., Kuanar, S. K., Kumar, L., Padhy, N., & Satapathy, S. C. (2020) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.3233/kes-190421 · Cited by 6
“Przemyslaw et al. [16] discussed the software cost and project estimation by using unique machine learning algorithms Padhy et al. [17] identified the reusable components from the Component-Based System.”
A Study of Software Development Cost Estimation Techniques and Models
Rashid, J., Nisar, M. W., Mahmood, T., Rehman, A. U., & Syed, Y. A. (2020) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/10.22581/muet1982.2002.18 · Cited by 6
“The results are generated from the multilayer neural system [46].”
BackgroundMethodAnalysis of automated estimation models using machine learning
Martínez, J. I. S., Souto, F. V., & Monje, M. R. (2020) · International Conference in Software Engineering Research and Innovation (CONISOFT) · doi:10.1109/conisoft50191.2020.00025 · Cited by 6
“Models based on artificial intelligence have been used to improve estimation accuracy, basically with machine learning techniques [12][13].”
MethodComparative Analysis of Machine Learning Techniques for Software Effort Estimation
Goyal, S. R. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-0252-9_7 · Cited by 6
Anitha, C., & Parveen, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18120-3 · Cited by 5
Ensemble Learning Approach for Effective Software Development Effort Estimation with Future Ranking
Rao, K. E., Pydi, B., Naidu, P. A., Prasann, U. D., & Anjaneyulu, P. (2023) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31206 · Cited by 5
Opportunities for Application of Artificial Intelligence in Telecommunication Projects
Dimcheva, G. S. (2024) · International Conference on Electronics, Engineering Physics and Earth Science (EEPES) · doi:10.3390/engproc2024070018 · Cited by 5
Nishat, M. M., Ahsan, A., & Olsson, N. O. E. (2025) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2025.033 · Cited by 8
On the Software Projects’ Duration Estimation Using Support Vector Regression
Hai, V. V., Javed, M., Abbas, Z., Ari, M., & Bílá, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-21435-6_25 · Cited by 5
Learning Software Project Management From Analyzing Q&A’s in the Stack Exchange
Ahmadi, A., Delkhosh, F., Deshpande, G., Patterson, R. A., & Ruhe, G. (2023) · IEEE Access · doi:10.1109/access.2023.3235953 · Cited by 11
“Using three ML algorithms (SVM, neural networks, and generalized linear models) and cross-validation, Pospieszny et al. [19] built a decision support tool for effort and duration estimation for organizations that develop or implement software systems.”
MethodSoftware Cost Estimation using Stacked Ensemble Classifier and Feature Selection
Hammad, M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140621 · Cited by 5
“Several scholars as [1] have suggested various models to predict the effort of software development.”
BackgroundAI Decision Assistant ChatBot for Software Release Planning and Optimized Resource Allocation
Ebrahim, E., Sayed, M., Youssef, M., Essam, H., El-Fattah, S. A., Ashraf, D., Magdy, O., & ElAdawi, R. (2023) · IEEE International Conference on Artificial Intelligence Testing (AITest) · doi:10.1109/aitest58265.2023.00018 · Cited by 5
4GL Code Generation: A Systematic Review
Alzahrani, A. A. H. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110623 · Cited by 5
Amasaki, S., Aman, H., & Yokogawa, T. (2022) · Empirical Software Engineering · doi:10.1007/s10664-021-10103-4 · Cited by 5
“For instance, efforts of models based on different algorithms were averaged for estimation (Pospieszny et al. 2018), and a suitable regression model was selected dynamically (de Cabral and JTH 2021).”
MethodMahmudova, S. (2019) · International Conference on Technics, Technologies and Education ICTTE ... · doi:10.15547/ictte.2019.04.061 · Cited by 5
Early Size Estimation using Machine Learning
Manisha, M., & Rishi, R. (2021) · International Conference on Computing for Sustainable Global Development · doi:10.1109/indiacom51348.2021.00135 · Cited by 5
“The performance has been evaluated in terms of MMRE, MAE, RMSE and PREDs [32].”
MethodAI applications in software functional size measurement: A systematic literature review
Czarnacka-Chrobot, B. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112686 · Cited by 4
Estimation Approaches of Machine Learning in Scrum Projects: A Review
Dave, C. V. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.38977 · Cited by 4
An Enhanced Metaheuristic Based Cuckoo Search Algorithm for Software Size Estimation
Manisha, M., & Rishi, R. (2021) · International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE) · doi:10.1109/rdcape52977.2021.9633575 · Cited by 4
Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines
Ikram, A., Jalil, M. A., Ngah, A. B., Raza, S., Khan, A. S., Mahmood, Y., Kama, N., Azmi, A., & Alzayed, A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.030818 · Cited by 4
EsArCost: Estimating repair costs of software architecture erosion using slice technology
Wang, T., & Li, B. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111875 · Cited by 4
An Efficient Framework for Cost and Effort Estimation of Scrum Projects
Dave, C. V. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.39030 · Cited by 4
“(2018) in their paper [1] make use of three approaches viz.”
BackgroundModeling and Representation of Software Framework Cost Estimation Using Convolution Neural Networks
Kumar, A., & Patro, B. D. K. (2021) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon52037.2021.9702391 · Cited by 4
Effort Estimation of Back-end Part of Software using Chaotically Modified Genetic Algorithm
Bilgaiyan, S., Goswami, D. K., Mishra, S., & Das, M. (2019) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2019.01.04 · Cited by 4
“The process of developing software is getting more and more complex day by day because nowadays the software projects are bigger in size and more complex unlike in the past where they were simple and less amount of requirements were also less in size [4, 5, 6].”
BackgroundYazılım Proje Çaba Tahmini için İdeal Yapay Sinir Ağları Parametrelerinin Belirlenmesi
Kayakuş, M. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.847712 · Cited by 4
Sharma, A., & Chaudhary, N. (2022) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2022.2.06 · Cited by 4
Agile Software Development Effort Estimation: A Comprehensive Survey
Malik, V., Arora, R., Mehrotra, D., & Singh, H. (2024) · International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN) · doi:10.1109/emergin63207.2024.10961288 · Cited by 3
“In paper [8] authors propose A machine learning-based approach using an ensemble of SVM, ANN, and GLM models improved software project effort and duration estimation over traditional methods.”
Chopra, D., & Kaur, A. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04162-x · Cited by 3
Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation
Moradbeiky, A., Bardsiri, V. K., & Shahbazzadeh, M. J. (2020) · Computing and Informatics · doi:10.31577/cai_2020_6_1148 · Cited by 3
Studi Literature Optimasi Waktu dan Biaya pada Proyek Perangkat Lunak
Parlika, R., Azizah, D., Latifah, S., & Hadi, B. D. (2021) · RESEARCH Computer Information System & Technology Management · doi:10.25273/research.v4i2.6636 · Cited by 3
“Namun dari sekian banyak proyek perangkat lunak, kendala yang sering terjadi yaitu terkait waktu dan biaya dikarenakan berbagai faktor seperti sumber daya yang terbatas atau timeline dari konsumen yang kurang toleransi[5][6][7].”
A Comparison Study About Parameter Optimization Using Swarm Algorithms
Carvalho, H. D. P. D., Soares, W. L., Santos, W. B., & Fagundes, R. (2022) · IEEE Access · doi:10.1109/access.2022.3175202 · Cited by 3
“For example, the Support Vector Regression (SVR) algorithm can model linear and nonlinear problems; however, its training process can be time-consuming with large volumes of data [10].”
BackgroundTandon, P., Suman, U., & Rathore, M. (2022) · International Journal of Computer Applications · doi:10.5120/ijca2022922238 · Cited by 3
Prediction of a Sprint Delivery’s Capabilities in Iterative-based Software Development
Hady, C. E. B., & Kusumo, D. S. (2023) · JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) · doi:10.29100/jipi.v8i1.3292 · Cited by 3
Lalitha, R., & Sreelekha, P. (2023) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s021819402350016x · Cited by 3
ENSEMBLE MACHINE LEARNING AND SOFTWARE DEVELOPMENT EFFORT ESTIMATION
Gora, R. K., & Sinha, R. R. (2023) · Journal of Analysis and Computations · doi:10.30696/jac.xvii.1.2023.41-46 · Cited by 3
Phan, V.-H., & Ly, H. (2024) · Journal of Science and Transport Technology · doi:10.58845/jstt.utt.2024.en.4.1.58-71 · Cited by 3
A Review: Effort Estimation Model for Scrum Projects using Supervised Learning
Sunda, N., & Sinha, R. R. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i11s.8102 · Cited by 3
A Systematic Literature Review on Software Development Estimation Techniques
Srivastava, P., Srivastava, N., Agarwal, R., & Singh, P. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-4641-6_11 · Cited by 3
Boon, G. C., & Stettina, C. J. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08169-9_8 · Cited by 3
Inteligencia artificial en la gestión de proyectos: caso construcción y obra civil
Jaimes-Quintanilla, M., & Zabala-Vargas, S. A. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-1615 · Cited by 7
Bbadana, M., Kumar, B., Sundari, M. R., Ramya, P. N., & Rao, G. S. V. R. K. (2025) · SN Computer Science · doi:10.1007/s42979-025-04459-3 · Cited by 2
“These advantages minimize the likelihood of software failure and control project delays [11].”
Yapay Zeka Teknikleri Kullanılarak Proje Üretim Sistemlerinin Tasarımı ve Geliştirilmesi
Özgür, A. S., Tarhan, Ç., Komesli, M., & Tecim, V. (2023) · Journal of Information Systems and Management Research · doi:10.59940/jismar.1214440 · Cited by 2
Application of a Size Measurement Standard for Data Warehouse Projects
Ünlü, H., Yürüm, O. R., Yıldız, A., & Demirörs, O. (2024) · Software Practice and Experience · doi:10.1002/spe.3391 · Cited by 2
“These algorithms can provide a more precise effort estimation [33].”
Review of AI in Project Management and Conceptual Analysis on Risk Management
Öztemel, E., & Tuncer, Y. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70935-7_17 · Cited by 2
A DNN Anti-Predatory Algorithm-Based Model to Enhance the Efficiency of Software Effort Estimation
Archana, S., & Rajpoot, D. S. (2025) · International Journal of Performability Engineering · doi:10.23940/ijpe.25.01.p2.1023 · Cited by 2
Sharma, A., & Rajpoot, D. S. (2025) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-025-00599-0 · Cited by 2
“Therefore, there is a crucial need for innovative techniques to enhance accurate precision in effort estimation and reduce financial losses for the Software Industry [8, 9].”
Marchinares, A. E. H., Herrera, J., Rivera, L., Rodriguez, C. R., & Krupa, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3859-5_5 · Cited by 2
Prykhodko, S. B., Pukhalevych, A., Prykhodko, K. S., & Makarova, L. M. (2022) · Radio Electronics Computer Science Control · doi:10.15588/1607-3274-2022-3-14 · Cited by 2
Modification of the PERT method for project time evaluation taking into account unexpected delays
Litvinov, V., & Moskaliuk, A. (2018) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2018.140752 · Cited by 2
Amasaki, S., Aman, H., & Yokogawa, T. (2020) · doi:10.1145/3416508.3417118 · Cited by 2
“Pospieszny [43] also used average efforts of models based on different algorithms.”
MethodAyat, M., Kim, B., & Kang, C. W. (2022) · International Journal of Construction Management · doi:10.1080/15623599.2022.2045862 · Cited by 2
“…neural networks (ANN), SVM and ensemble methods (Sanni-Anibire et al. 2020), and ensemble algorithms consists of SVM, ANN, and logistic regression (Pospieszny et al. 2018) have used for estimation of project cost, efforts, and duration, risk assessment and classification of accidents in…”
BackgroundMethodSoftware Size Estimation: A survey
AL-Saleem, A. L., & Hammo, A. Y. (2022) · Technium Romanian Journal of Applied Sciences and Technology · doi:10.47577/technium.v4i9.7251 · Cited by 2
“5 "An effective approach for software project effort and duration estimation with machine learning algorithms " [28].”
MethodA reverse personnel assignment method with duration re-inferring for smart “IOT+ blockchain” project
Zhang, L., Guo, W., Yang, W., Su, D., & Li, S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.374 · Cited by 2
Use Case Points based software effort prediction using regression analysis
Ardiansyah, A., Ferdiana, R., & Permanasari, A. E. (2019) · International Conference on Advanced Computer Science and Information Systems · doi:10.1109/icacsis47736.2019.8979851 · Cited by 2
“However, time and cost overruns were serious problems for software organizations [3], [4].”
BackgroundUsing Ensemble Machine Learning Methods in Estimating Software Development Effort
Kanneganti, A. (2020) · Diva portal (Dalarna University Library) · Cited by 2
Software Effort Estimation using Ensemble LearningPreprint
Farah Oshaibi, M., AlKhanafseh, M., & Surakhi, O. (2024) · Preprints.org · doi:10.20944/preprints202403.0437.v1 · Cited by 2
Devadas, M., & Samuel, P. (2024) · Network Computation in Neural Systems · doi:10.1080/0954898x.2024.2376703 · Cited by 1
“COCOMO became one of the most prominent estimating models in the 1980s as a result of Boehm’s publication.”
InfluentialPoonia, R. C., Upreti, K., & Alam, M. S. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-9515-4_21 · Cited by 1
Kaya, E. N., & Görmez, Y. (2025) · Mühendislik Bilimleri ve Tasarım Dergisi · doi:10.21923/jesd.1604190 · Cited by 1
dos Santos, R. B., & Martins, L. E. G. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70158 · Cited by 1
“…superior performance in scenarios characterized by large data volumes and high complexity, as highlighted by Iordan [22] and Pospieszny et al. [103], who report gains in accuracy, stability, and generalization capability when compared with individual or traditional approaches. consolidates…”
PROJECT PERFORMANCE PREDICTION BASED ON MACHINE LEARNING: A SYSTEMATIC LITERATURE REVIEW
Rehman, F. A. U., Jebna, A. K., Ayyasamy, R. K., & Senadjki, A. (2025) · Journal of Information System and Technology Management · doi:10.35631/jistm.1041026 · Cited by 1
Wijaya, D. C. C., Sabrina, A. L., & Meiliana (2025) · IEEE International Conference on Cryptography, Informatics, and Cybersecurity (ICoCICs) · doi:10.1109/icocics68032.2025.11383851 · Cited by 1
“The methodology of this research was to address the limitations identified in prior studies on effort or story point estimation in agile projects [22].”
Advancing project success prediction through machine learning-driven models
Ojiako, U., Iqbal, M., Dacre, N., Baxter, D., Dong, H., Qazi, A. J., Chipulu, M., & Al-Mhdawi, M. K. S. (2026) · Production Planning & Control · doi:10.1080/09537287.2026.2676289 · Cited by 3
Analysis of the cost factors on E-government software cost using fuzzy decision making system
Gu, X., Li, G., Cao, S., Zhang, Y., & Wang, R. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189638 · Cited by 1
The Role of Artificial Intelligence in Project Performance in Construction Companies in Palestine
Alrifai, K., Obaid, T., Ali, A. A. A., Abulehia, A. F. S., Abualrejal, H. M., & Nassoura, M. B. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16865-9_6 · Cited by 1
Comparative Analysis of Ensemble Models for Software Effort Estimation
Jawa, M., & Meena, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5292-0_5 · Cited by 1
López-Martín, C., & Meda-Campaña, M. E. (2022) · International Conference on Data and Software Engineering · doi:10.1109/icodse56892.2022.9971876 · Cited by 1
“The variables mostly predicted have been size [2], effort [3], duration [4], and quality [5].”
BackgroundChopra, D., & Khurana, R. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815124422123010012 · Cited by 1
Varshini, A. G. P., & Kumari, K. A. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-230676 · Cited by 1
“A statistical model called the generalized linear model (GLM) [23] uses maximum likelihood estimation to assess the parameters.”
RetractedYasmin, A., Haider, W., Daud, A. Y., & Banjar, A. (2023) · Intelligent Data Analysis · doi:10.3233/ida-237366 · Cited by 1
“SDEE study conducted by [62] utilized ensemble model to estimate effort and duration required for software.”
Cost Adjustment for Software Crowdsourcing Tasks Using Ensemble Effort Estimation and Topic Modeling
Yasmin, A. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-08746-8 · Cited by 1
“Although previous SDEE studies draw conclusions on limited samples as well [20, 86], more samples can be effective for training ML models.”
Integrated Prediction Model for Software Effort of Diversifying Projects Using Machine Learning
Sharma, A., Chaudhary, N., & Hotchandani, B. (2023) · International Conference on Intelligent Systems and Embedded Design (ISED) · doi:10.1109/ised59382.2023.10444589 · Cited by 1
“Estimation models MMRE HSDEEM (Proposed model) 0.094 RBFNN[12] 0.17 Ensemble model [21] 0.17 Regression[23] 0.34 FAHP[22] 0.57 CBR [20] 0.45”
MethodInfluentialDeciphering the Realities of Deep Learning in Business Analytics
Mukherjee, S., Chatterjee, A., & Dass, S. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-1598-9.ch004 · Cited by 1
Software Effort Estimation Using Deep Learning: A Gentle Review
Meenakshi, & Pareek, M. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-0327-2_26 · Cited by 1
Diverse Bagging Effort Estimation Model for Software Development Project
Haris, M., Chua, F.-F., & Lim, A. H.-L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64608-9_19 · Cited by 1
Sunda, N., & Sinha, R. R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3810-6_21 · Cited by 1
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_9 · Cited by 1
Valuation of software cost and time estimation methods: Asystematic review of the literature
Margita, S., Sediyono, E., Prasetyo, S. Y. J., Iriani, A., Hartomo, K. D., Sembiring, I., & Purnomo, H. D. (2025) · AIP conference proceedings · doi:10.1063/5.0258574 · Cited by 1
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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