Abstract
Software defect prediction (SDP) plays an important role to help software development, where various advanced intelligence algorithms but no firefly algorithm (FA) are used to improve the prediction accuracy within a project or across projects. Current FA faces the problem of many unnecessary movements which reduces its efficiency of searching for an optimal solution. Therefore, an improved multiple swarms with different strategies firefly algorithm is proposed, named MSFA. The key principle of MSFA is to divide the swarm into three groups, where each group plays a different role to balance exploration and exploitation. Experimental studies were tested on CEC 2013 and SDP. The test results on CEC 2013 prove that MSFA achieves a high balance between the exploration and the exploitation. The test results conducted on SDP show that MSFA has a higher prediction accuracy, but much less computation cost compared with other FA variants.
Supported by the National Natural Science Foundation of China (No. 61763019).
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Cao, L., Ben, K., Peng, H., Zhang, X., Wang, F. (2021). An Improved Firefly Algorithm for Software Defect Prediction. In: He, K., Zhong, C., Cai, Z., Yin, Y. (eds) Theoretical Computer Science. NCTCS 2020. Communications in Computer and Information Science, vol 1352. Springer, Singapore. https://doi.org/10.1007/978-981-16-1877-2_3
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