BACKGROUND: Research on software effort estimation has been active for decades, especially in developing effort estimation models. Effort estimation models need a dataset collected from completed projects similar to a project to be estimated. The similarity suffers from dataset shift, and cross-company software effort estimation (CCSEE) gets an attractive research topic. A recent study on the dataset shift problem examined the applicability of cross-project defect prediction (CPDP) approaches. It was insufficient to bring a conclusion regarding the applicability due to a limited number of examined approaches. AIMS: To investigate the characteristics of CPDP approaches that are effective for dataset shift problem in effort estimation. METHOD: We first reviewed the characteristics of 24 CPDP approaches to find applicable approaches. Next, we investigated their effort estimation performance with ten dataset configurations. RESULTS: 16 out of 24 CPDP approaches implemented in CrossPare framework were found to be applicable to CCSEE. However, only one approach could improve the effort estimation performance. Most of the others degraded it and were harmful. CONCLUSIONS: Most of the CPDP approaches we examined were helpless for CCSEE.