The reporting of large number of duplicate bug reports has generated the need for appropriate duplicate bug report detection techniques. Researchers have developed duplicate bug report detection techniques using different approaches such as Information Retrieval, Machine Learning etc. However, due to rapid development of duplicate detection techniques, it has become difficult to compare and select an appropriate duplicate bug report detection technique. Besides, the usage of different Information Retrieval and Machine Learning techniques have made it more difficult to understand the successes, failures and future opportunities of the proposed techniques. In order to draw a clear picture of the existing techniques developed from the inception to the present, this paper presents a systematic analysis of the duplicate bug report detection techniques. The analysis has been prepared from existing techniques published in ranked conference and journals. The paper has presented insights on the type of input data set used for developing and testing the techniques, the feature selection and pre-processing strategies of bug reports and the type of algorithms and evaluation metrics used for developing the techniques. The paper lastly elaborates the findings established during the discussion of the insights, and presents a road map for future research on the uncovered areas.