An Artificial Neural Network Model based on Binary Particle Swarm Optimization for enhancing the efficiency of Software Defect Prediction

Author:

Malhotra Ruchika1ORCID,Chawla Sonali1ORCID,Sharma Anjali2ORCID

Affiliation:

1. Department of Software Engineering, Delhi Technological University, India

2. Planning Monitoring and Evaluation, CSIR-National Physical Laboratory, India

Publisher

ACM

Reference34 articles.

1. A survey on software fault detection based on different prediction approaches;Abaei Golnoush;Vietnam Journal of Computer Science,2014

2. Software defect prediction using cost-sensitive neural network;Arar Ömer Faruk;Applied Soft Computing Journal 33,2015

3. Proposed approach to predict software faults detection using Entropy;Banga Manu;International Journal of Systems Assurance Engineering and Management 11,2020

4. Andre B. de Carvalho , Aurora Pozo , Silvia Vergilio , and Alexandre Lenz . 2008 . Predicting fault proneness of classes trough a multiobjective particle swarm optimization algorithm . In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 387–394 . DOI:https://doi.org/10.1109/ICTAI.2008.76 10.1109/ICTAI.2008.76 Andre B. de Carvalho, Aurora Pozo, Silvia Vergilio, and Alexandre Lenz. 2008. Predicting fault proneness of classes trough a multiobjective particle swarm optimization algorithm. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 387–394. DOI:https://doi.org/10.1109/ICTAI.2008.76

5. A symbolic fault-prediction model based on multiobjective particle swarm optimization;de Carvalho André B.;Journal of Systems and Software,2010

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