Background. The bug assignment problem is the problem of triaging new bug reports to the most qualified developer. The qualified developer is the one who has enough knowledge in a specific area that is relevant to the reported bug. In recent years, bug triaging has received a considerable amount of attention from researchers. In previous work, bugs were represented as vectors of terms extracted from the bug reports' description. Once the bugs are represented as vectors in the terms space, traditional machine learning techniques are employed for the bug assignment. Most of the previous algorithms are marred by low accuracy values. Aims. This paper formulates the bug assignment problem as a classification task, and then examines the impact of several term selection approaches on the classification effectiveness. Method. Three variants selection methods that are based on the Log Odds Ratio (LOR) score are compared against methods that are based on the Information Gain (IG) score and Latent Semantic Analysis (LSA). The main difference in the methods that are based on the LOR score is in the process of selecting the terms. Results. Term selection techniques that are based on the Log Odds Ratio achieved up to 30% improvement in the precision and up to 5% higher in recall compared to other term selection methods such as Latent Semantic Analysis and Information Gain. Conclusions. Experimental results showed that the effectiveness of bug assignment methods is directly affected by the selected terms that are used in the classification methods.