An effective approach for software project effort and duration estimation with machine learning algorithms
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There are some examples of using ML algorithms for project management.
“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
Cites
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,655 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189 · see citing works
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works
(1996) · Cited by 4,738 · see citing works
A guide to the project management body of knowledge (PMBOK guide)
Pmbok (2004) · Cited by 4,335 · see citing works
(2015) · Cited by 2,857 · see citing works
Methods of combining multiple classifiers and their applications to handwriting recognition
Xu, L., Krzyżak, A., & Suen, C. Y. (1992) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.155943 · Cited by 2,192 · see citing works
Neural Network Ensembles, Cross Validation, and Active Learning
Krogh, A., & Vedelsby, J. (1994) · Cited by 1,838 · see citing works
Software Cost Estimation With Cocomo II
BOEHM, B. W., Abts, C., Brown, A. W., Chulani, S., Clark, B., Horowitz, E., Madachy, R. J., Reifer, D. J., & Steece, B. M. (2000) · Cited by 1,774 · see citing works
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Linoff, G. S., & Berry, M. J. (2004) · Cited by 1,240 · see citing works
Software engineering metrics and models
Conte, S. D., Dunsmore, H. E., & Shen, V. Y. (1986) · Benjamin-Cummings Publishing Co., Inc. eBooks · Cited by 1,062 · see citing works
Information and Software Technology
Achimugu, P., & Selamat, A. (2014) · Cited by 780 · see citing works
A systematic review of software development cost estimation studies
Jørgensen, M., & Shepperd, M. (2007) · Brunel University Research Archive (BURA) (Brunel University London) · doi:10.1109/tse.2007.3 · Cited by 748 · see citing works
A Systematic Review of Software Development Cost Estimation Studies
Jørgensen, M., & Shepperd, M. (2006) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2007.256943 · Cited by 736 · see citing works
Information Technology Project Management
Schwalbe, K. (1999) · Cited by 657 · see citing works
Systematic literature review of machine learning based software development effort estimation models
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2011) · Information and Software Technology · doi:10.1016/j.infsof.2011.09.002 · Cited by 531 · see citing works
Software engineering metrics and models
Vanamstel, J. J. (1987) · European Journal of Operational Research · doi:10.1016/0377-2217(87)90230-x · Cited by 521 · see citing works
Data Mining Methods and Models
Larose, D. T. (2005) · Wiley eBooks · doi:10.1002/0471756482 · Cited by 511 · see citing works
From Nobel Prize to Project Management: Getting Risks Right
Flyvbjerg, B. (2006) · Project Management Journal · doi:10.1177/875697280603700302 · Cited by 498 · see citing works
Experiment with a new boosting algorithm
Freund, Y. (1996) · Medical Entomology and Zoology · Cited by 486 · see citing works
A simulation study of the model evaluation criterion mmre
Foss, T., Stensrud, E., Kitchenham, B. A., & Myrtveit, I. (2003) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2003.1245300 · Cited by 480 · see citing works
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310 · see citing works
The rise and fall of the Chaos report figures
Eveleens, J. L., & Verhoef, C. (2009) · IEEE Software · doi:10.1109/ms.2009.154 · Cited by 284 · see citing works
Principles and Theory for Data Mining and Machine Learning
Clarke, B. S., Fokoué, E., & Zhang, H. H. (2009) · Springer series in statistics · doi:10.1007/978-0-387-98135-2 · Cited by 272 · see citing works
On the Value of Ensemble Effort Estimation
Kocagüneli, E., Menzies, T., & Keung, J. (2011) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.111 · Cited by 269 · see citing works
Software cost estimation with incomplete data
Strike, K., Emam, K. E., & Madhavji, N. H. (2001) · IEEE Transactions on Software Engineering · doi:10.1109/32.962560 · Cited by 252 · see citing works
Data Mining and Predictive Analytics
McCue, C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800229-2.00003-1 · Cited by 251 · see citing works
Software Estimation: Demystifying the Black Art
McConnell, S. (2006) · Cited by 248 · see citing works
Project management and its effects on project success: Cross-country and cross-industry comparisons
de Carvalho, M. M., Patah, L. A., & Bido, D. D. S. (2015) · International Journal of Project Management · doi:10.1016/j.ijproman.2015.04.004 · Cited by 243 · see citing works
An empirical analysis of data preprocessing for machine learning-based software cost estimation
Huang, J., Li, Y.-F., & Xie, M. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2015.07.004 · Cited by 223 · see citing works
Integration of the grey relational analysis with genetic algorithm for software effort estimation
Huang, S.-J., Chiu, N., & Chen, L. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2007.07.002 · Cited by 221 · see citing works
Reliability of function points measurement : a field experiment
Kemerer, C. F. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 219 · see citing works
Information technology project management
Chmielarz, W. S. (2015) · doi:10.7172/978-83-65402-07-3.2015.wwz.4 · Cited by 213 · see citing works
Gido, J., & Clements, J. P. (1998) · Medical Entomology and Zoology · Cited by 208 · see citing works
Using public domain metrics to estimate software development effort
Jeffery, R., Ruhe, M., & Wieczorek, I. (2001) · IEEE International Software Metrics Symposium (METRICS) · doi:10.1109/metric.2001.915512 · Cited by 186 · see citing works
A Model of Project Knowledge Management
Gasik, S. (2011) · Project Management Journal · doi:10.1002/pmj.20239 · Cited by 176 · see citing works
Software Measurement and Estimation
Laird, L. M., & Brennan, M. C. (2006) · Wiley eBooks · doi:10.1002/0471792535 · Cited by 168 · see citing works
Savolainen, P., Ahonen, J. J., & Richardson, I. (2011) · International Journal of Project Management · doi:10.1016/j.ijproman.2011.07.002 · Cited by 168 · see citing works
Glass, R. L. (2006) · Communications of the ACM · doi:10.1145/1145287.1145301 · Cited by 166 · see citing works
Ensembles and locality: Insight on improving software effort estimation
Minku, L. L., & Yao, X. (2012) · Information and Software Technology · doi:10.1016/j.infsof.2012.09.012 · Cited by 154 · see citing works
Perceived causes of software project failures – An analysis of their relationships
Lehtinen, T. O. A., Mäntylä, M., Vanhanen, J., Itkonen, J., & Lassenius, C. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.01.015 · Cited by 149 · see citing works
Reliability of function points measurement
Kemerer, C. F. (1993) · Communications of the ACM · doi:10.1145/151220.151230 · Cited by 147 · see citing works
Tronto, Í. F. D. B., da Silva, J. D. S., & Sant’Anna, N. (2007) · Journal of Systems and Software · doi:10.1016/j.jss.2007.05.011 · Cited by 134 · see citing works
(2015) · Cited by 118 · see citing works
Software Sizing, Estimation, and Risk Management
Galorath, D. D., & Evans, M. W. (2006) · Auerbach Publications eBooks · doi:10.1201/9781420013122 · Cited by 109 · see citing works
Port, D., & de Korte, M. (2008) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1145/1414004.1414015 · Cited by 107 · see citing works
Research patterns and trends in software effort estimation
Sehra, S. K., Brar, Y. S., Kaur, N., & Sehra, S. S. (2017) · Information and Software Technology · doi:10.1016/j.infsof.2017.06.002 · Cited by 95 · see citing works
Improved estimation of software development effort using Classical and Fuzzy Analogy ensembles
Idri, A., Hosni, M., & Abran, A. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.08.012 · Cited by 85 · see citing works
Finding conclusion stability for selecting the best effort predictor in software effort estimation
Keung, J., Kocagüneli, E., & Menzies, T. (2012) · Automated Software Engineering · doi:10.1007/s10515-012-0108-5 · Cited by 76 · see citing works
Cross-validation aggregation for combining autoregressive neural network forecasts
Barrow, D. K., & Crone, S. F. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2015.12.011 · Cited by 75 · see citing works
Why Do Information Technology Projects Fail?
Alami, A. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.09.124 · Cited by 72 · see citing works
Comprehensible software fault and effort prediction: A data mining approach
Moeyersoms, J., de Fortuny, E. J., Dejaeger, K., Baesens, B., & Martens, D. (2014) · Journal of Systems and Software · doi:10.1016/j.jss.2014.10.032 · Cited by 71 · see citing works
López‐Martín, C. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.10.033 · Cited by 70 · see citing works
The usage of ISBSG data fields in software effort estimation: A systematic mapping study
González-Ladrón-de-Guevara, F., Fernández-Diego, M., & Lokan, C. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2015.11.040 · Cited by 69 · see citing works
Principles and Theory for Data Mining and Machine Learning
Kleine, L. L. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_3.x · Cited by 63 · see citing works
Wateridge, J. (2001) · International Journal of Project Management · doi:10.1016/s0263-7863(99)00054-x · Cited by 62 · see citing works
Comparison of estimation methods of cost and duration in IT projects
Berlin, S., Raz, T., Glezer, C., & Zviran, M. (2008) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.007 · Cited by 61 · see citing works
Validity and reliability of evaluation procedures in comparative studies of effort prediction models
Myrtveit, I., & Stensrud, E. (2011) · Empirical Software Engineering · doi:10.1007/s10664-011-9183-7 · Cited by 57 · see citing works
Neural networks for predicting the duration of new software projects
López‐Martín, C., & Abran, A. (2014) · Journal of Systems and Software · doi:10.1016/j.jss.2014.12.002 · Cited by 52 · see citing works
Intelligent Data Mining: Techniques and Applications
Ruan, D. (2010) · SCK CEN Institutional Repository · Cited by 51 · see citing works
López‐Martín, C., Isaza, C., & Chavoya, A. (2011) · Empirical Software Engineering · doi:10.1007/s10664-011-9192-6 · Cited by 51 · see citing works
Software Project Effort Estimation: Foundations and Best Practice Guidelines for Success
Trendowicz, A., & Jeffery, R. (2014) · Springer eBooks · doi:10.1007/978-3-319-03629-8 · Cited by 44 · see citing works
Using Ensembles for Web Effort Estimation
Azhar, D., Riddle, P., Mendes, E., Mittas, N., & Angelis, L. (2013) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1109/esem.2013.25 · Cited by 41 · see citing works
Fernández-Diego, M., & González-Ladrón-de-Guevara, F. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.01.003 · Cited by 40 · see citing works
KumarSingh, B., & Misra, A. K. (2012) · International Journal of Computer Applications · doi:10.5120/9577-4053 · Cited by 37 · see citing works
A framework for comparing multiple cost estimation methods using an automated visualization toolkit
Mittas, N., Mamalikidis, I., & Angelis, L. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.05.010 · Cited by 35 · see citing works
Data Mining Methods and Models
Booth, D. E. (2007) · Technometrics · doi:10.1198/tech.2007.s697 · Cited by 33 · see citing works
State of the Practice in Software Effort Estimation: A Survey and Literature Review
Trendowicz, A., Münch, J., & Jeffery, R. (2016) · Cited by 33 · see citing works
Wilkie, F. G., McChesney, I., Morrow, P., Tuxworth, C., & Lester, N. G. (2011) · Information and Software Technology · doi:10.1016/j.infsof.2011.05.008 · Cited by 30 · see citing works
The Rosetta Stone Making COCOMO 81 Estimates Work with COCOMO II
Reifer, D. J., BOEHM, B. W., & Chulani, S. (1999) · Cited by 22 · see citing works
Software Stage-Effort Estimation Based on Association Rule Mining and Fuzzy Set Theory
Azzeh, M., Cowling, P., & Neagu, D. C. (2010) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2010.76 · Cited by 19 · see citing works
Software Effort Estimation Using a Neural Network Ensemble
Pai, D. R., McFall, K. S., & Subramanian, G. H. (2013) · Journal of Computer Information Systems · doi:10.1080/08874417.2013.11645650 · Cited by 19 · see citing works
Software Measurement and Estimation: A Practical Approach
(2007) · Kybernetes · doi:10.1108/03684920710743439 · Cited by 18 · see citing works
Application Of Data Mining Tools To Predicate Completion Time Of A Project
Iranmanesh, S. H., & Mokhtari, Z. (2008) · doi:10.5281/zenodo.1057164 · Cited by 15 · see citing works
Neural network based software effort estimation & evaluation criterion MMRE
Dave, V. S., & Dutta, K. (2011) · International Conference on Computer and Communication Technology · doi:10.1109/iccct.2011.6075192 · Cited by 15 · see citing works
A Data Mining Model to Predict Software Bug Complexity Using Bug Estimation and Clustering
Nagwani, N. K., & Bhansali, A. (2010) · doi:10.1109/itc.2010.56 · Cited by 15 · see citing works
Shukla, R., Shukla, M., Misra, A. K., Marwala, T., & Clarke, W. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31128-4_12 · Cited by 14 · see citing works
Software estimation: towards prescriptive analytics
Pospieszny, P. (2017) · Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement · doi:10.1145/3143434.3143459 · Cited by 14 · see citing works
Self-citationProceedings Seventh International Software Metrics Symposium
(2001) · doi:10.1109/metric.2001 · Cited by 12 · see citing works
Automated function points: critical evaluation and discussion
Lavazza, L. (2015) · Workshop on Emerging Trends in Software Metrics · doi:10.5555/2821491.2821500 · Cited by 10 · see citing works
Pospieszny, P., Czarnacka-Chrobot, B., & Kobyliński, A. (2015) · Lecture notes in business information processing · doi:10.1007/978-3-319-24285-9_7 · Cited by 9 · see citing works
Data Mining Applied to the Improvement of Project Management
Villanueva, J., Montequín, V. R., Ortega, F., & Alba, C. (2012) · InTech eBooks · doi:10.5772/48734 · Cited by 7 · see citing works
Zastosowanie technik eksploracji danych do estymacji pracochłonności projektów informatycznych
Kobyliński, A., & Pospieszny, P. (2015) · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 89 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Artificial Intelligence Enabled Project Management: A Systematic Literature Review
Taboada, I., Daneshpajouh, A., Toledo, N., & de Vass, T. (2023) · Applied Sciences · doi:10.3390/app13085014 · Cited by 223 · see citing works
“An effective ML ensemble model composed of SVM, NN, and Generalized Linear Models is provided in [32].”
MethodGiray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187 · see citing works
Meharie, M. G., Mengesha, W. J., Gariy, Z. A., & Mutuku, R. N. (2021) · Engineering Construction & Architectural Management · doi:10.1108/ecam-02-2020-0128 · Cited by 83 · see citing works
Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation
Khan, M. S., Jabeen, F., Ghouzali, S., Rehman, Z., Naz, S., & Abdul, W. (2021) · IEEE Access · doi:10.1109/access.2021.3072380 · Cited by 80 · see citing works
“MBRE better handles outliers as it chastens underestimation and overestimation on the same level [12].”
BackgroundMethodEstimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach
G, P. V. A., Kumari, K. A., & Varadarajan, V. (2021) · Electronics · doi:10.3390/electronics10101195 · Cited by 76 · see citing works
“[30] ISBSG 1192 projects 13 attributes ensemble averaging of 3 ML models -SVM(support vector machines) MLP(multilayer perceptron) -GLM(general linear model) MAE, MSE, and RMSE MMRE PRED MMER- Mean magnitude relative error to estimate MBRE-mean of balanced relative error The dataset used for estimation was the ISBSG da…”
MethodBig Data analytics in Agile software development: A systematic mapping study
Biesialska, K., Franch, X., & Muntés-Mulero, V. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106448 · Cited by 64 · see citing works
Software Project Management Using Machine Learning Technique—A Review
Mahdi, M. N., Zabil, M. H. M., Ahmad, A. R., Ismail, R., Yusoff, Y., Cheng, L. K., Azmi, M. S. M., Natiq, H., & Naidu, H. H. (2021) · Applied Sciences · doi:10.3390/app11115183 · Cited by 62 · see citing works
“The authors of [83–85] presented a test framework of the source code metrics and chose the best metric set for the model’s performance.”
BackgroundExtreme Learning Machine Applied to Software Development Effort Estimation
Carvalho, H. D. P. D., Fagundes, R., & Santos, W. B. (2021) · IEEE Access · doi:10.1109/access.2021.3091313 · Cited by 53 · see citing works
“Therefore, to minimize uncertainties during the software development cycle, Machine Learning (ML) techniques have been used to predict software development efforts over the past few years [3], [13].”
BackgroundMethodZabala-Vargas, S. A., Jaimes-Quintanilla, M., & Jimenez-Barrera, M. H. (2023) · Buildings · doi:10.3390/buildings13122944 · Cited by 52 · see citing works
Comparative analysis of Machine learning and Deep learning algorithms for Software Effort Estimation
Varshini, A. G. P., Kumari, K. A., Janani, D., & Soundariya, R. S. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1767/1/012019 · Cited by 46 · see citing works
“Name of the paper Datasets used Algorithm used Evaluation measures Advantages Disadvantages [7] Software duration and Effort”
MethodA Systematic Literature Review of Machine Learning Estimation Approaches in Scrum Projects
Arora, M., Verma, S., Kavita, K., & Chopra, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1451-7_59 · Cited by 45 · see citing works
A pragmatic ensemble learning approach for effective software effort estimation
Kumar, P. S., Behera, H. S., Nayak, J., & Naik, B. (2021) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-020-00379-y · Cited by 44 · see citing works
Saputri, T. R. D., & Lee, S.-W. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106407 · Cited by 45 · see citing works
Adamantiadou, D. S., & Tsironis, L. K. (2025) · Computers · doi:10.3390/computers14020066 · Cited by 41 · see citing works
Evaluation of estimation in software development using deep learning-modified neural network
Sreekanth, N., Devi, J. R., Shukla, K. A., Mohanty, D. K., Srinivas, A., Rao, G. N., Alam, A., & Gupta, A. (2022) · Applied Nanoscience · doi:10.1007/s13204-021-02204-9 · Cited by 41 · see citing works
Using an Artificial Neural Network for Improving the Prediction of Project Duration
Lishner, I., & Shtub, A. (2022) · Mathematics · doi:10.3390/math10224189 · Cited by 47 · see citing works
“ANN performance was found superior compared to other multiple regression models in project effort estimation [55], and an algorithm for software project prediction [56] exists but does not fit other types of projects and is validated only with the quite old ISBSG public dataset.”
MethodAn efficient ANFIS-EEBAT approach to estimate effort of Scrum projects
Arora, M., Verma, S., Kavita, K., Woźniak, M., Shafi, J., & Ijaz, M. F. (2022) · Scientific Reports · doi:10.1038/s41598-022-11565-2 · Cited by 39 · see citing works
“Fuzzy logic 8 , Decision Trees (DT), Bayesian Networks (BN) with reviews attempted in the field of effort and cost estimation 10,14,15 .”
BackgroundMethodEnsemble Effort Estimation using dynamic selection
Cabral, J., & Oliveira, A. L. I. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110904 · Cited by 32 · see citing works
Linear Regression Model for Agile Software Development Effort Estimation
Sharma, A., & Chaudhary, N. (2020) · IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) · doi:10.1109/icraie51050.2020.9358309 · Cited by 32 · see citing works
“The learning-oriented include the techniques such as artificial intelligence, neural network [7], machine learning [8], case-based reasoning [9], optimization techniques [10], fuzzy logic [11], etc.”
MethodA predictive model to estimate effort in a sprint using machine learning techniques
Ramessur, M. A., & Nagowah, S. D. (2021) · International Journal of Information Technology · doi:10.1007/s41870-021-00669-z · Cited by 32 · see citing works
Machine Learning Models for Software Cost Estimation
Asheeri, M. A., & Hammad, M. (2019) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict.2019.8910327 · Cited by 32 · see citing works
“As the process of software cost estimation is rapidly evolving which may include technology advances, team skills and experience, and tools and programming languages available, it gives superiority to ML techniques than some other methods that may stick to statistical and mathematical work [2].”
MethodPredictive analytics approaches for software effort estimation: A review
Varshini, A. G. P. (2020) · Indian Journal of Science and Technology · doi:10.17485/ijst/v13i21.573 · Cited by 31 · see citing works
“According to Expert judgment method (2), estimation is produced based on the judgmental process.”
MethodToward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis
Hai, V. V., Nhung, H. L. T. K., Prokopová, Z., Šilhavý, R., & Šilhavý, P. (2022) · IEEE Access · doi:10.1109/access.2022.3185393 · Cited by 31 · see citing works
Applicability of Neural Network Based Models for Software Effort Estimation
Shukla, S., & Kumar, S. (2019) · IEEE World Congress on Services (SERVICES) · doi:10.1109/services.2019.00094 · Cited by 31 · see citing works
“So, the main objective here is to estimate the effort required to develop software either with the help of algorithmic models or non algorithmic models [8].”
BackgroundInfluentialShop floor simulation optimization using machine learning to improve parallel metaheuristics
de Sousa, W. T., Montevechi, J. A. B., Miranda, R. D. C., de Oliveira, M. L. M., & Campos, A. T. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113272 · Cited by 30 · see citing works
Hassan, C. A. U., Khan, M. S., Irfan, R., Iqbal, J., Hussain, S., Ullah, S. S., Alroobaea, R., & Umar, F. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3145956 · Cited by 29 · see citing works
“In recent years, it has been the average value of balanced relative error (MBRE) in software estimation research [12].”
BackgroundMethodA Non-Linear Technique for Effective Software Effort Estimation using Multi-Layer Perceptrons
Goyal, S. R., & Bhatia, P. K. (2019) · International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) · doi:10.1109/comitcon.2019.8862256 · Cited by 28 · see citing works
“MultiLayer Perceptron is one of the most popular and overly used type of neural network for effort prediction [10], [11], [12].”
MethodHoc, H. T., Šilhavý, R., Prokopová, Z., & Šilhavý, P. (2022) · IEEE Access · doi:10.1109/access.2022.3215987 · Cited by 28 · see citing works
“Furthermore, the experience of software developers, the software team’s project history in the same business domain, and a variety of other characteristics, as well as the relationships between these factors, are sometimes not accurately accounted for [9].”
BackgroundMethodEfficiency Improvement of Function Point-Based Software Size Estimation With Deep Learning Model
Zhang, K., Wang, X., Ren, J., & Liu, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998581 · Cited by 27 · see citing works
“To obtain a more precise and portable estimation model, a nonparametric model such as machine learning techniques is applied to the estimation model by training on historical project data, such as a neural network (NN) and its extension [11]–[14].”
BackgroundMethodRETRACTED ARTICLE: Hyperparameters tuning of ensemble model for software effort estimation
Palaniswamy, S. K., & Venkatesan, R. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02277-4 · Cited by 27 · see citing works
“For Software Estimation which is basically a regression measure, the most appropriate criteria for error measurement are MMRE and PRED(x), apart from the standard error metrics used, such as Mean Absolute Error and Mean Square Error (Pospieszny et al. 2018).”
MethodRetractedSousa, A., Veloso, D. T., Gonçalves, H., Faria, J. P., Mendes‐Moreira, J., Graça, R., Gomes, D., Castro, R. S. E., & Henriques, P. C. (2023) · IEEE Access · doi:10.1109/access.2023.3307310 · Cited by 27 · see citing works
“[5] obtained predictive models with a very good performance for estimating the total project effort and duration, on a dataset provided by the International Software Benchmarking Standards Group (ISBSG).”
BackgroundMethodInfluentialBarbalho, S. C. M., de Carvalho, M. M., Tavares, P. D. M., Llanos, C. H., & Leite, G. A. (2019) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2936502 · Cited by 26 · see citing works
Feature Selection Technique for Effective Software Effort Estimation Using Multi-Layer Perceptrons
Goyal, S. R., & Bhatia, P. K. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-30577-2_15 · Cited by 26 · see citing works
Hassanali, M., Soltanaghaei, M., Gandomani, T. J., & Boroujeni, F. Z. (2024) · Journal of Software Evolution and Process · doi:10.1002/smr.2665 · Cited by 31 · see citing works
“39 When there is a high correlation between dependent and independent features, and a low correlation between independent features, the accuracy of the machine learning model improves.”
Predict the required cost to develop Software Engineering projects by Using Machine Learning
Abdulmajeed, A. A., Al-jawaherry, M. A., & Tawfeeq, T. M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1897/1/012029 · Cited by 25 · see citing works
“It gives progression to machine learning techniques over than some other techniques that might stick to arithmetical and statistical work [3].”
BackgroundMarco, R., Ahmad, S. S. S., & Ahmad, S. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130351 · Cited by 25 · see citing works
“Meanwhile, standard SEE methodologies can produce erroneous estimates due to human bias and subjectivity [7].”
BackgroundExploring product complexity and prototype lead-times to predict new product development cycle-times
Bolanos, R. D. S., & Barbalho, S. C. M. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2021.108077 · Cited by 24 · see citing works
A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation
Hai, V. V., Nhung, H. L. T. K., Prokopová, Z., Šilhavý, R., & Šilhavý, P. (2022) · Computers · doi:10.3390/computers11020015 · Cited by 24 · see citing works
Pandey, M., Litoriya, R., & Pandey, P. (2020) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194020500023 · Cited by 24 · see citing works
Machine learning and deep learning in project analytics: methods, applications and research trends
Uddin, S., Yan, S., & Lu, H. (2024) · Production Planning & Control · doi:10.1080/09537287.2024.2320790 · Cited by 23 · see citing works
A State of the Art Regressor Model’s comparison for Effort Estimation of Agile software
Arora, M., Sharma, A., Katoch, S., Malviya, M., & Chopra, S. (2021) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem51511.2021.9445345 · Cited by 22 · see citing works
“For instance, when a similar ML model is built with distinct datasets [3] [8] or scenarios [9], the estimation accuracy changes.”
ResultA random forest model for early-stage software effort estimation for the SEERA dataset
Mustafa, E. I., & Osman, R. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107413 · Cited by 20 · see citing works
Role of Soft Computing Techniques in Software Effort Estimation: An Analytical Study
Kumar, P. S., & Behera, H. S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-9042-5_70 · Cited by 20 · see citing works
Terlapu, P. V., Raju, K. K., Kumar, G. K., Rao, G. J., Kavitha, K., & Samreen, S. (2024) · IEEE Access · doi:10.1109/access.2024.3457771 · Cited by 19 · see citing works
Bakhshi, R., Moradinia, S. F., Jani, R., & Poursorkhabi, R. V. (2022) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-022-1424-3 · Cited by 19 · see citing works
“The developed models could serve as a support tool for estimating the required effort and time by organizations where software tools are being developed or deployed (Pospieszny et al., 2018).”
MethodEstimating Software Functional Size via Machine Learning
Lavazza, L., Locoro, A., Liu, G., & Meli, R. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3582575 · Cited by 22 · see citing works
Systems Thinking in Software Projects-an Artificial Neural Network Approach
Mitrović, Z., Rakićević, A., Petrović, D. Č., Mihic, M. M., Rakičević, J., & Jelisic, E. (2020) · IEEE Access · doi:10.1109/access.2020.3040169 · Cited by 20 · see citing works
“[29]–[33]), duration estimation [33], and cost estimation (e.”
BackgroundComparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation
Hoc, H. T., Šilhavý, R., Prokopová, Z., & Šilhavý, P. (2023) · IEEE Access · doi:10.1109/access.2023.3286372 · Cited by 18 · see citing works
“Moreover, the expertise of software engineers, the software team’s project history in the same business area, and a range of other features, as well as the correlations between these aspects, are not always accounted for appropriately [9].”
BackgroundSoftware Effort Estimation Using Machine Learning Techniques
Sinha, R. R., & Gora, R. K. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-5421-6_8 · Cited by 18 · see citing works
Artificial Intelligence in Project Management
Dacre, N., & Kockum, F. (2022) · doi:10.61175/dogx9829 · Cited by 18 · see citing works
Software Measurements Using Machine Learning Techniques - A Review
Goyal, S. R. (2022) · Recent Advances in Computer Science and Communications · doi:10.2174/2666255815666220407101922 · Cited by 17 · see citing works
Software Effort Estimation with Machine Learning – A Systematic Literature Review
Ritu, R., & Bhambri, P. (2023) · doi:10.1002/9781119896838.ch15 · Cited by 17 · see citing works
An Artificial Neural Network Model for Project Effort Estimation
Şengüneş, B., & Öztürk, N. (2023) · Systems · doi:10.3390/systems11020091 · Cited by 17 · see citing works
“The most commonly used activation functions for hidden layers in the field of effort estimation are sigmoid [8,19,21], hyperbolic tangent [9,20,35], and tansig [29].”
BackgroundMethodInfluentialJadhav, A., & Shandilya, S. K. (2023) · Journal of Engineering Research · doi:10.1016/j.jer.2023.100150 · Cited by 17 · see citing works
Aguilar, R. M., Torres, J., & Martín, C. A. (2018) · Revista Iberoamericana de Automática e Informática Industrial RIAI · doi:10.4995/riai.2018.9421 · Cited by 17 · see citing works
“Finalmente corresponde descubrir qué algoritmo de ML es el más adecuado al problema a tratar (Pospieszny et al., 2018).”
BackgroundUddin, S., Ong, S., Lu, H., & Matouš, P. (2023) · Production Planning & Control · doi:10.1080/09537287.2023.2196256 · Cited by 16 · see citing works
Yasmin, A., Butt, W. H., & Daud, A. Y. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300296 · Cited by 16 · see citing works
Ensemble Regression Models for Software Development Effort Estimation: A Comparative Study
Carvalho, H. D. P., Lima, M. N. C. A., Santos, W. B., & de A.Fagunde, R. A. (2020) · International Journal of Software Engineering & Applications · doi:10.5121/ijsea.2020.11305 · Cited by 16 · see citing works
“Today, using data mining techniques, in particular, machine learning (ML) algorithm, is used for effort prediction minimizing uncertainties [5].”
MethodAn Effective Nature Inspired Approach for the Estimation of Software Development Cost
Hassan, C. A. U., & Khan, M. S. (2021) · International Conference on Emerging Technologies · doi:10.1109/icet54505.2021.9689832 · Cited by 15 · see citing works
Kumar, K. H., & Srinivas, K. K. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03427-y · Cited by 15 · see citing works
Effective Software Effort Estimation using Heterogenous Stacked Ensemble
Goyal, S. R. (2022) · IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · doi:10.1109/spices52834.2022.9774231 · Cited by 15 · see citing works
Analyzing Effect of Ensemble Models on Multi-Layer Perceptron Network for Software Effort Estimation
Shukla, S., Kumar, S., & Bal, P. R. (2019) · IEEE World Congress on Services (SERVICES) · doi:10.1109/services.2019.00116 · Cited by 15 · see citing works
The Last State of Artificial Intelligence in Project ManagementPreprint
Davahli, M. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12262 · Cited by 15 · see citing works
“[42] Initiating Integration Effort estimation Support vector machines, neural networks, and generalized linear models [43] Initiating Integration Project success Evolutionary project success prediction model (EPSPM) [44] Initiating Integration Effort prediction Artificial neural network, simple linear regression and m…”
MethodThe C-BA method: enhancing megaproject forecasting through the “Fifth Hand” principle
Mariani, C., Cellerino, F., Aliaga, E. H. A., Atencio, E., & Mancini, M. (2025) · International Journal of Managing Projects in Business · doi:10.1108/ijmpb-11-2024-0281 · Cited by 15 · see citing works
Estimation of Risk Contingency Budget in Projects using Machine Learning
Capone, C., & Narbaev, T. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.10.140 · Cited by 14 · see citing works
Assessing the impact of artificial intelligence on project efficiency enhancement
Kozhakhmetova, A. K., Mamyrbayev, A., Zhidebekkyzy, A., & Bilan, S. (2024) · Knowledge and Performance Management · doi:10.21511/kpm.08(2).2024.09 · Cited by 18 · see citing works
“For instance, Pospieszny et al. (2018) stated that AI techniques accurately estimate the effort required in software projects.”
Su, Y., Han, L., Wang, H., & Wang, J. (2019) · Enterprise Information Systems · doi:10.1080/17517575.2019.1700551 · Cited by 14 · see citing works
Effort Estimation in Agile Software Development using Evolutionary CostSensitive Deep Belief Network
Premalatha, H., & Srikrishna, C. (2019) · International journal of intelligent engineering and systems · doi:10.22266/ijies2019.0430.25 · Cited by 14 · see citing works
“P. Pospieszny, B. Czarnacka-Chrobot, and A. Kobylinski [19] developed an ensemble three machine learning such as Support Vector Machine (SVM), NN and Generalized Linear Models (GLM).”
BackgroundMethodInfluentialHsu, M., Dacre, N., & Senyo, P. K. (2021) · SSRN Electronic Journal · doi:10.31235/osf.io/6hfje · Cited by 14 · see citing works
Error reduction in long-term mine planning estimates using deep learning models
Aguilera, C. O. D., Campos, P. G., & Risso, N. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119487 · Cited by 13 · see citing works
SENSE: software effort estimation using novel stacking ensemble learning
Kaushik, A., Sheoran, K., Kapur, R., Bhutani, N., Singh, B., & Sharma, H. (2024) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-024-00581-2 · Cited by 13 · see citing works
“Pospieszny et al. [21] utilised Neural Networks, Support Vector Machines, and Generalised Linear Models for their software project effort and duration estimation research.”
Project Portfolio Management Studies Based on Machine Learning and Critical Success Factors
Marchinares, A. E. H., & Alonso, I. A. (2020) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic50277.2020.9350787 · Cited by 13 · see citing works
Gouda, S. K., & Mehta, A. K. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00882-4 · Cited by 13 · see citing works
“Models based on machine learning and meta-heuristic evolution techniques have been used to estimate the effort and cost of software projects in past few decades as technology has improved [9, 14, 21, 26].”
MethodRetractedSharma, A., & Chaudhary, N. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.138 · Cited by 13 · see citing works
Particle Swarm Optimization for Predicting the Development Effort of Software Projects
Alanis-Tamez, M. D., López-Martín, C., & Villuendas-Rey, Y. (2020) · Mathematics · doi:10.3390/math8101819 · Cited by 13 · see citing works
“The software project planning includes software prediction, and the most common predicted variables have been size [2] (mainly measured in either source lines of code, or function points [3]), effort (in person-hours or person-months [3]), duration (in months [4]), and quality (in defects [5]).”
MethodA Systematic Literature Review of Machine Learning Techniques for Software Effort Estimation Models
Brar, P., & Nandal, D. (2022) · International Conference on Computational Intelligence and Communication Technologies (CCICT) · doi:10.1109/ccict56684.2022.00093 · Cited by 12 · see citing works
“Proposed ensemble model provide better results [35].”
Charmjuree, T., Badir, Y. F., & Safdar, U. (2021) · European Journal of Innovation Management · doi:10.1108/ejim-07-2020-0263 · Cited by 12 · see citing works
Software Development Effort Estimation Techniques Using Long Short Term Memory
Ahmad, F. B., & Ibrahim, L. M. (2022) · doi:10.1109/csase51777.2022.9759751 · Cited by 12 · see citing works
Comparative Analysis of Machine Learning Techniques in Effort Estimation
Ritu, R., & Garg, Y. (2022) · International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) · doi:10.1109/com-it-con54601.2022.9850592 · Cited by 12 · see citing works
Analysis of Software Effort Estimation Based on Story Point and Lines of Code using Machine Learning
Sharma, A., & Chaudhary, N. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1201012 · Cited by 12 · see citing works
“[3] suggested an ensemble model based on three machine learning techniques: support vector machine (SVM), Multilayer Perceptron (MLP), and Generalized Linear Models (GLM) (GLM).”
MethodBodendorf, F., Lutz, M., & Franke, J. E. (2021) · Managerial and Decision Economics · doi:10.1002/mde.3336 · Cited by 11 · see citing works
“Following the ideas of Pospieszny et al. (2018), Venkataiah et al. (2019) propose a non-parametric approach using an Artificial Neural Network in order to improve the accuracy of process cost estimation which is improving the quality of the software product.”
BackgroundMethodMachine learning for mHealth apps quality evaluation
Haoues, M., Mokni, R., & Sellami, A. (2023) · Software Quality Journal · doi:10.1007/s11219-023-09630-8 · Cited by 10 · see citing works
“Different software development and maintenance activities could be expressed through learning problems and solved by learning algorithms such as effort estimation Pospieszny et al. (2018), requirements classification Zhang and Tsai (2002), and so on.”
MethodEffort prediction for the software project construction phase
López‐Martín, C. (2021) · Journal of Software Evolution and Process · doi:10.1002/smr.2365 · Cited by 10 · see citing works
Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation
Li, Y., Ren, Z. M., Wang, Z., Yang, L., Dong, L., Zhong, C., & Zhang, H. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3623349 · Cited by 10 · see citing works
Software Cost Estimation for Python Projects Using Genetic Algorithm
Sharma, A., & Chaudhary, N. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3325-9_11 · Cited by 10 · see citing works
Enhanced framework for ensemble effort estimation by using recursive‐based classification
Azath, H., Raja, M., Pandimurugan, V., Krishnan, S., & Rajendran, L. (2021) · IET Software · doi:10.1049/sfw2.12020 · Cited by 10 · see citing works
“The main objective is to make a difference between research results and implementations within organisations by proposing useful research finding and best practices of the industry [6].”
BackgroundSoftware Requirements Elicitation Techniques Selection Method for the Project Scope Management.
Gobov, D., & Huchenko, I. (2021) · Cited by 10 · see citing works
A Review of Machine Learning Models for Software Cost Estimation
Arslan, F. (2019) · Review of Computer Engineering Research · doi:10.18488/journal.76.2019.62.64.75 · Cited by 10 · see citing works
“Pospieszny, et al. [2] developed an approach based on machine learning algorithms to limit the gap between recent researches and real implementations.”
BackgroundMethodSudharson, K., Raj, K. R. M., Selvi, K., Kumar, A. S., Dhakshunhaamoorthiy, & Murali, E. (2023) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses60034.2023.10465271 · Cited by 9 · see citing works
“Pospieszny et al., state-of-the-art review [9] focused on machine learning in adaptive project management.”
BackgroundSarwar, H., & Rahman, M. M. (2024) · IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) · doi:10.1109/seai62072.2024.10674089 · Cited by 9 · see citing works
Ali, U., & Naseer, M. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00578-6 · Cited by 10 · see citing works
“For instance, in the study where Pospieszny (2018) compared the use of ML algorithms, such as SVM and ensemble methods with function point analysis to estimate the effort, ML algorithms proved to be much more accurate compared to function point analysis which is a mostly inaccurate effort…”
InfluentialShanbhag, S., & Chimalakonda, S. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3560883 · Cited by 9 · see citing works
“Examples include automated identification of non-functional requirements [6, 29], effort estimation [23], classification of issue reports [22], automatic tagging of pull requests [14], clone detection [32] etc.”
BackgroundMethodA Proposal for Research on the Application of AI/ML in ITPM
Mishra, A., Tripathi, A., & Khazanchi, D. (2022) · International Journal of Information Technology Project Management · doi:10.4018/ijitpm.315290 · Cited by 9 · see citing works
“These mechanisms can be used to increase revenue and decrease operating costs, which is the goal of any organization (Pospieszny et al., 2018).”
BackgroundKumar, B. K., Bilgaiyan, S., & Mishra, B. S. P. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140222 · Cited by 9 · see citing works
“[28] proposed ensemble averaging with a 3-fold validation, namely SVM, MLP, and GLM, to predict both effort and duration.”
MethodNhung, H. L. T. K., Vo, H. V., Silhavy, P., Prokopova, Z., & Silhavy, R. (2023) · J. Softw. Evol. Process. · doi:10.1002/smr.2611 · Cited by 10 · see citing works
Software Effort Estimation Using Multilayer Perceptron and Long Short Term Memory
PREDESCU, E.-F., Stefan, A., & ZAHARIA, A.-V. (2019) · Informatica Economica · doi:10.12948/issn14531305/23.2.2019.07 · Cited by 9 · see citing works
“There are already efforts of researching the effectiveness of ML algorithms for project management and our search has led us to a few publications [2],[3],[4].”
BackgroundResultA hybrid model for prediction of software effort based on team size
Rai, P., Verma, D. K., & Kumar, S. (2021) · IET Software · doi:10.1049/sfw2.12048 · Cited by 9 · see citing works
Software Development Effort Estimation Techniques: A Survey
alhamdany, F., & Ibrahim, L. M. (2022) · Mağallaẗ al-tarbiyaẗ wa-al-ʻilm · doi:10.33899/edusj.2022.132274.1201 · Cited by 9 · see citing works
“[12] ISBSG Ensemble averaging of three ML algorithms -General Linear model.”
MethodResultInfluentialA Study of Software Development Cost Estimation Techniques and Models
Rashid, J., Nisar, M. W., Mahmood, T., Rehman, A. U., & Arafat, S. Y. (2020) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/muet1982.2002.18 · Cited by 9 · see citing works
Hsu, M., Dacre, N., & Senyo, P. K. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3880328 · Cited by 9 · see citing works
“The short-term memory characteristic of recurrent neural networks can be used to recognise the inter-project relationship which cannot be recognised with MLP commonly used in the literature (López-Martín & Abran, 2015; Pospieszny et al., 2018).”
BackgroundMethodInfluential
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
