The delivery of defect-free products is always being a challenge in the software industry. Limitation of testing criteria is reasoned as important aspects that lead to the existence of faults/bugs in the developed system. However, fault and effort prediction is a futuristic event in any software development-planning phase. Nevertheless, to save time, effort and budget forecasting faults and effort become critical aspects of software development. It has been proven that unsupervised and semi-supervised classification techniques produce more accurate results in the lack of availability of past information. To reduce the manual intervention of experts for identifying modules, authors propose an automatic software tool with a semi-supervised feature based on a self-organizing map to detect labels using reduced map size. Three different scenarios, which integrate proposed clustering with regression-based classification, are the main contribution of the study. The fusion of clustering and regression improves the capability of the prediction model in the presence of heterogeneous data. The use of feature subset selection is also considered with an experimental comparison. The combination of feature selection with the proposed technique provides more flexibility to choose a significant amount of attributes.
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28 April 2023
COMPUTATIONAL INTELLIGENCE AND NETWORK SECURITY
3–4 March 2022
Raipur (C.G), India
Research Article|
April 28 2023
Hybrid semi-supervised SOM based clustered approach with genetic algorithm for software fault classification Available to Purchase
Aarti;
Aarti
a)
1
Lovely Professional University
, Phagwara, Punjab, INDIA
, 144411a)Corresponding author: [email protected]
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Pushpendra Kumar Rajput;
Pushpendra Kumar Rajput
b)
2
School of Computer Science, University of Petroleum and Energy Studies
, Dehradun, Uttarakhand, INDIA
, 248007
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Ankit Khare
Ankit Khare
c)
3
Himalayan School of Science & Technology, Swami Rama Himalayan University
, Dehradun, Uttarakhand, INDIA
, 248016
Search for other works by this author on:
Aarti
1,a)
Pushpendra Kumar Rajput
2,b)
Ankit Khare
3,c)
1
Lovely Professional University
, Phagwara, Punjab, INDIA
, 144411
2
School of Computer Science, University of Petroleum and Energy Studies
, Dehradun, Uttarakhand, INDIA
, 248007
3
Himalayan School of Science & Technology, Swami Rama Himalayan University
, Dehradun, Uttarakhand, INDIA
, 248016AIP Conf. Proc. 2724, 020002 (2023)
Citation
Aarti, Pushpendra Kumar Rajput, Ankit Khare; Hybrid semi-supervised SOM based clustered approach with genetic algorithm for software fault classification. AIP Conf. Proc. 28 April 2023; 2724 (1): 020002. https://doi.org/10.1063/5.0141332
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