Machine learning techniques for software testing effort prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Safety, Risk, Reliability and Quality,Software
Link
http://link.springer.com/content/pdf/10.1007/s11219-020-09545-8.pdf
Reference92 articles.
1. Abhilasha, Sharma, A. (2013). Test effort estimation in regression testing. In: IEEE International Conference in MOOC, Innovation and Technology in Education (MITE), Jaipur, India, pp. 343-348. https://doi.org/10.1109/MITE.2013.6756364.
2. Abhishek, C., Kumar, V. P., Vitta, H., & Srivastava, P. R. (2010). Test effort estimation using neural network. Journal Software Engineering & Applications, 3, 331–340. https://doi.org/10.4236/jsea.2010.34038.
3. Ali, A., Gravino, C. (2019). A systematic literature review of software effort prediction using machine learning methods, Journal of Software: Evolution and Process, Wiley, 31(10), e2211. https://doi.org/10.1002/smr.2211.
4. Almeida, É.R.C., Abreu, B.T., Moraes, R. (2009). An alternative approach to test effort estimation based on use cases. In: IEEE International Conference on Software Testing Verification and Validation, pp. 279–288. https://doi.org/10.1109/ICST.2009.31.
5. Aloka, S., Singh, P., Rakshit, G., Srivastava, P.R. (2011). Test effort estimation-particle swarm optimization based approach, In: Communications in Computer and Information Science, pp. 463–474. https://doi.org/10.1007/978-3-642-22606-9_46.
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