Many software artifacts are written in natural language or contain substantial amount of natural language contents. Thus these artifacts could be analyzed using text analysis techniques from the natural language processing (NLP) community, e.g., the part-of-speech (POS) tagging technique that assigns POS tags (e.g., verb, noun, etc.) to words in a sentence. In the literature, several studies have already applied POS tagging technique on software artifacts to recover important words in them, which are then used for automating various tasks, e.g., locating buggy files for a given bug report, etc. There are many POS tagging techniques proposed and they are trained and evaluated on non software engineering corpus (documents). Thus it is unknown whether they can correctly identify the POS of a word in a software artifact and which of them performs the best. To fill this gap, in this work, we investigate the effectiveness of seven POS taggers on bug reports. We randomly sample 100 bug reports from Eclipse and Mozilla project and create a text corpus that contains 21,713 words. We manually assign POS tags to these words and use them to evaluate the studied POS taggers. Our comparative study shows that the state-of-the-art POS taggers achieve an accuracy of 83.6%-90.5% on bug reports and the Stanford POS tagger and the TreeTagger achieve the highest accuracy on the sampled bug reports. Our findings show that researchers could use these POS taggers to analyze software artifacts, if an accuracy of 80-90% is acceptable for their specific needs, and we recommend using the Stanford POS tagger or the TreeTagger.