Embeddings are known for their ability to understand semantic relationships, reduce dimensionality, and identify patterns in data. These techniques are mostly used in machine learning as they are helpful and can easily be integrated into prediction models. Embedding techniques such as Word2Vec and TF-IDF are commonly used for software defect prediction tasks. While creating a defect prediction model, picking the suitable embedding method is very important. This study aims to compare the two commonly used embedding techniques, Word2Vec and TF-IDF, in the context of software defect prediction. Different sets of Java projects taken from an open-source Promise repository are used for conducting the entire analysis. A thorough evaluation was carried out by training and assessing multiple deep learning models. The efficacy of both techniques was evaluated using several evaluation metrics, including Matthews correlation coefficient, specificity, accuracy, and other significant performance indicators. Based on various metrics, the results show that TF-IDF outperforms Word2Vec, demonstrating its superiority in software defect prediction.