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An Unsupervised Learning Based Conceptual Coupling Measure.

, , , and . SYNASC, page 247-254. IEEE Computer Society, (2017)

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Source-Code Embedding-Based Software Defect Prediction., and . ICSOFT, page 185-196. SCITEPRESS, (2023)New Conceptual Cohesion Metrics: Assessment for Software Defect Prediction.. SYNASC, page 163-170. IEEE, (2021)A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks., , and . Inf. Sci., (2018)An analysis of aggregated coupling's suitability for software defect prediction., and . SYNASC, page 141-148. IEEE, (2020)COMET: A conceptual coupling based metrics suite for software defect prediction., , and . KES, volume 176 of Procedia Computer Science, page 31-40. Elsevier, (2020)Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code., and . KSEM (1), volume 11775 of Lecture Notes in Computer Science, page 262-274. Springer, (2019)On the Relevance of Graph2Vec Source Code Embeddings for Software Defect Prediction., and . ICSOFT (Selected Papers), volume 2104 of Communications in, page 124-154. Springer, (2023)Detecting depression from fMRI using relational association rules and artificial neural networks., and . ICCP, page 85-92. IEEE, (2017)DynGRAR: A dynamic approach to mining gradual relational association rules., and . KES, volume 159 of Procedia Computer Science, page 10-19. Elsevier, (2019)An Unsupervised Learning Based Conceptual Coupling Measure., , , and . SYNASC, page 247-254. IEEE Computer Society, (2017)