A deep intelligent framework for software risk prediction using improved firefly optimization-Reference-Cited by-同舟云学术

A deep intelligent framework for software risk prediction using improved firefly optimization

Published:2023-06-29 Issue:26 Volume:35 Page:19523-19539
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Pemmada Suresh Kumar,Nayak Janmenjoy,Naik Bighnaraj

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08756-x.pdf

Reference37 articles.

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2. Suresh Kumar P, Behera HS, Nayak J, Naik B (2021) A pragmatic ensemble learning approach for effective software effort estimation. Innov Syst Softw Eng. https://doi.org/10.1007/s11334-020-00379-y

3. The Economic impacts of inadequate infrastructure of software testing. RTI. https://www.nist.gov/system/files/documents/director/planning/report02-3.pdf

4. Rosen C, Grawi B, Shihab E (2015) Commit guru: analytics and risk prediction of software commits. In: Proceedings of the 2015 10th joint meeting on foundations of software engineering–ESEC/FSE 2015, p 966–969. https://doi.org/10.1145/2786805.2803183

5. Drew Procaccino J, Verner JM, Overmyer SP, Darter ME (2002) Case study: factors for early prediction of software development success. Inf Softw Technol 44(1):53–62. https://doi.org/10.1016/S0950-5849(01)00217-8

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