## 6 Conclusions  
The performance of a defect prediction model is influenced by the sample size of dataset, selection of classifiers and data resampling methods. From our empirical study, we compared performance of nine popular classifiers in 29 software datasets with different sample size ranging from 100 to 20,000 to study the influence of sample size and classifiers. The major conclusion in this part is that GNB performs well with small datasets, but its performance deteriorates when sample size of datasets grow to 1100. Another classifier ABC performs stable with different sample size and obtains relatively better result with large datasets among classifiers. On this basis, in order to make an expected matching on small datasets, we proposed a new resampling method LIMCR motivated by the good performance of GNB. LIMCR is used for SDP datasets with small sample size and it is designed as the best resampling method cooperated with classifier GNB. The results of comparison experiments confirm that the performance of LIMCR is better than the other resampling methods, and the matching between GNB and LIMCR is the best solution for the imbalance problem in SDP datasets with small datasets. Besides, we also design experiments to research how it performs with other classifiers, feature selection and data with large sample size. The results can be summarized as follows.  
* LIMCR together with classifier GNB is a better solution for the imbalance problem on SDP datasets with small datasets which is slightly better than SMOE+GNB.
* On aspect of metric G-mean, LIMCR has the same well performance when cooperates with other classifiers. On aspect of metric balancedscore, when cooperating with LIMCR, GNB performs significantly better than other classifiers.
* Number of features in a datasets has no influence on LIMCR, but the performance turn significantly worse when the number of features less than 5.
* When the sample size bigger than 1100, performance of LIMCR is worse than IHT, so when sample size bigger than 1100, IHT is recommended as the best imbalanced learning method for SDP.  
Although our proposed LIMCR cannot outperform for all datasets, but the result of our research emphasizes the importance of the influence of datasets. There is no all-purpose imbalance learning methods, the way of choosing methods appropriately is also important. In the future, we plan to extend our research to cover other data distribution problems such as overlapping problem and high dimensionality. We will update our LIMCR to solve more combined problems and being suitable for more SDP datasets.  
Conceptualization, Y.W.; methodology, Y.W. and J.Y.; software, J.Y.; validation, Y.W. and J.Y.; formal analysis, J.Y.; investigation, J.Y. and S.C.; resources, S.C.; data curation, S.C.; writing-original draft preparation, J.Y. and S.C.; writing-review and editing, Y.W.; visualization, J.Y.; supervision, B.L.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Aerospace Science Foundation of China (grant number 2017 ZD51052).  
The authors declare no conflict of interest.