With the variety of different filter methods, selecting the most appropriate filter method which gives the best performance is a difficult task. Filter rank selection and stability problems make the selection of filter methods in SDP a hard choice. The best approach is to independently apply a mixture of filter methods and evaluate the results. This study presents a novel rank aggregation-based multi-filter feature selection method to address high dimensionality and filter rank selection problems in software defect prediction. The proposed method combines the rank list generated by individual filter methods from the software defect dataset using a rank aggregation mechanism into a single aggregated rank list. The proposed method aims to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a more stable (non-disjoint) and complete feature rank list better than individual filter methods employed. The effectiveness of the proposed method was evaluated by applying with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed method had a superior effect (positive) on the prediction performance of selected prediction models than other experimented methods. This makes the combining of individual filter rank methods a viable solution to the filter rank selection problem and enhancement of prediction models in SDP. © 2021, Springer Nature Singapore Pte Ltd.