Fuzzy rule base systems are expert systems rely on fuzzy set theory. Here the knowledge of human expert is transfered to the artificial model via fuzzy rules. Therefore, preciseness, completeness and coverage of fuzzy rules in a fuzzy system is vital for the accuracy and plausibility of fuzzy reasoning. However, in such cases where the human expert is unable to supply the rules sufficiently, data-based automatic rule generation methods attract attention. In this study, 2 linear and 2 evolutionary approaches of automatic fuzzy rule generation methods are investigated. The investigated linear solutions contain Wang-Mendel Method and E2E-HFS, while MOGUL and IN/TURS-FARC are the selected evolutionary approaches. Wang Mendel and MOGUL is commonly considered as basic methods of the group they belong to. INTHRS-FARC is distinguished with its ability to handle interval valued fuzzy sets. Among the rest of the algorithms, E2E-HFS is unique with its weak dependency to data. Because it only use some simple properties of corresponding input variable. In order to compare the completeness and the accuracy of automatically generated fuzzy rules, several experiments are performed on different software defect prediction datasets, and the classification performance of resulting fuzzy systems is evaluated. Provided results show that even if training of evolutionary approaches seem to be more precise, similar accuracy can be achieved by linear approaches, and they perliwm better regarding the experiments on unseen data.