Comparative Analysis of Machine Learning Techniques for Software Effort Estimation-Reference-Cited by-同舟云学术

Comparative Analysis of Machine Learning Techniques for Software Effort Estimation

Published:2022 Issue: Volume: Page:63-73
ISSN:1876-1100
Container-title:Intelligent Computing Techniques for Smart Energy Systems
language:
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Author:

Goyal Somya

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-0252-9_7

Reference24 articles.

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2. Goyal S, Bhatia PK (2021) Empirical software measurements with machine learning. In: Bansal A, Jain A, Jain S, Jain V, Choudhary A (eds) Computational intelligence techniques and their applications to software engineering problems. CRC Press, Boca Raton, pp 49–64. https://doi.org/10.1201/9781003079996

3. Goyal S (2021) Handling class-imbalance with KNN (neighbourhood) under-sampling for software defect prediction. Artif Intell Rev. https://doi.org/10.1007/s10462-021-10044-w

4. Sehra SK, Brar YS, Kaur N, Sehra SS (2017) Research patterns and trends in software effort estimation. Inf Softw Technol 91:1–21

5. Goyal S (2021) Predicting the defects using stacked ensemble learner with filtered dataset. Autom Softw Eng 28:14. https://doi.org/10.1007/s10515-021-00285-y

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