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An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

https://doi.org/10.3390/math12020200

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1 of the 37 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1155/2022/8743694 (notice 2023-12-20).

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Artificial Intelligence Techniques to Enhance Cost, Effort, and Schedule Estimates in Software Development Projects: A Systematic Literature Review

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Hybrid Models for Agile Software Effort Estimation: Integrating LSTM, Ensemble Learning, and Genetic Algorithms

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SENSE: software effort estimation using novel stacking ensemble learning

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An Empirical Study on Small-Sized Datasets Based on Eubank’s Optimal Spacing Theorem

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Software development effort prediction using a hybrid of LeNet and Shepard convolutional neural networks

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Hierarchical multi-scale dense-RNN with sparse attention model for effort and cost estimation of IT projects in agile methodologies

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A Comparative Analysis of Different Machine Learning Techniques Used in Software Effort Estimation

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Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction

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Empirical Comparison of Neural Network Architectures for Prediction of Software Development Effort and Duration

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Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

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Hybrid Deep Neural Network Approaches for Power Quality Analysis in Electric Arc Furnaces

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