<p>Despite numerous approaches for code smell detection, the current cutting-edge techniques, such as transfer learning (TL), have not been thoroughly investigated in the domain of code smell detection. This study aims to explore the viability of TL for identifying code smells on homogeneous data by leveraging prior knowledge of previously detected code smells. To achieve this objective, the study has defined the problem of identifying code smells on homogeneous data using TL. The experiment utilized publicly accessible homogeneous datasets that include four types of code smells-long method (LM), data class (DC), god class (GC), and feature envy (FE) to assess the effectiveness of a TL technique called domain invariant transfer kernel learning (DITKL). The evaluation of DITKL's performance has been examined using four performance metrics: precision, recall, accuracy, and the area under the receiver operating characteristic curve. The findings indicated an absence of co-occurrence between GC and DC because of inadequate code smell detection from GC to DC and vice versa. Additionally, the findings indicated the viability of identifying code smells such as LM and FE and revealed a significant correlation between the two. This study suggests the effectiveness of using DITKL to identify instances of code smells and their co-occurrences in experimental datasets when two code smells are related. Therefore, the utilization of TL has the potential to aid in the detection of code smells on homogeneous data in situations where a code smell detection tool or expert knowledge is not accessible.</p>