Predicting software effort using BERT-based word embeddings

Sanoussi Maiga (), Saurabh Bilgaiyan () and Santwana Sagnika ()
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Sanoussi Maiga: KIIT,deemed to be university
Saurabh Bilgaiyan: KIIT,deemed to be university
Santwana Sagnika: KIIT,deemed to be university

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 5, No 4, 1728-1742

Abstract: Abstract Accurate software effort estimation is essential for effective project planning and resource allocation, particularly in Agile software development where evolving requirements challenge traditional methods. This study explores the potential of pre-trained BERT (Bidirectional Encoder Representations from Transformers) models, a state-of-the-art NLP technique, to improve estimation accuracy. We compare the performance of the BERT base and BERT large models in diverse project scenarios. The results show that BERT Base consistently outperforms BERT Large in cross-repository and project-based contexts, owing to its computational efficiency and adaptability. A combined CNN and BERT Base model further enhances story point prediction for new projects, achieving superior accuracy and robustness. These findings highlight the practical advantages of leveraging BERT Base in Agile environments, offering valuable insights for researchers, software developers, and project managers. Future work will focus on external validation using commercial datasets, alternative deep learning architectures, and improved fine-tuning strategies to further advance effort estimation practices.

Keywords: Software effort estimation; Agile software development; BERT (Bidirectional Encoder Representations from Transformers); Natural Language Processing (NLP); Deep learning; Story points prediction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02746-z

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