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FeSCH: A Feature Selection Method using Clusters of Hybrid-data for Cross-Project Defect Prediction.

, , , , and . COMPSAC (1), page 51-56. IEEE Computer Society, (2017)

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S2 LMMD: Cross-Project Software Defect Prediction via Statement Semantic Learning and Maximum Mean Discrepancy., , , , , and . APSEC, page 369-379. IEEE, (2021)Empirical Studies of a Two-Stage Data Preprocessing Approach for Software Fault Prediction., , , , , and . IEEE Trans. Reliability, 65 (1): 38-53 (2016)Learning contextual dissimilarity on tensor product graph for visual re-ranking., , and . Image Vision Comput., (2018)FeSCH: A Feature Selection Method using Clusters of Hybrid-data for Cross-Project Defect Prediction., , , , and . COMPSAC (1), page 51-56. IEEE Computer Society, (2017)FECS: A Cluster Based Feature Selection Method for Software Fault Prediction with Noises., , , , and . COMPSAC, page 276-281. IEEE Computer Society, (2015)A Two-Stage Data Preprocessing Approach for Software Fault Prediction., , , , , and . SERE, page 20-29. IEEE, (2014)A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction., , , , , and . J. Comput. Sci. Technol., 32 (6): 1090-1107 (2017)Improving Shape Retrieval by Fusing Generalized Mean First-Passage Time., , and . ICONIP (4), volume 10637 of Lecture Notes in Computer Science, page 439-448. Springer, (2017)FECAR: A Feature Selection Framework for Software Defect Prediction., , , , , and . COMPSAC, page 426-435. IEEE Computer Society, (2014)