The term bug triage refers to the process of assigning the suitable developer to fix the new incoming bugs which is considered to be one of the tedious process in software testing world than fixing a particular bug. The amount of new incoming bugs increases day-by-day, therefore manual bug triage becomes a tedious and imprecise for practical use thus increasing both the cost of development and time. In order to systematize the bug triage process, a wide number of data mining techniques can be used. Text Categorization is one of the effective method that helps in automating the system, where data reduction lies as one of the major difficulty identified in automating the bug triage process using text classification. Data reduction problem drops the exactness of automated bug triage prediction. This paper introduce a newly proposed framework entitled as Auto-BugTriager that aims to eliminate the data reduction problem better in range. The Auto-BugTriager framework is made up of three phase’s viz. InfoZie, DataReduction and NBPredictor which process in an organized manner in order to predict the list of expert developer who is capable of fixing the particular bug in short time. As per the experimental and theoretical analysis, the proposed system provides 92% of accuracy in predicting the correct developer to fix a bug. Therefore, it is strongly believed that the industry adopting this framework will be improved with software productivity and quality.

Bug Triage Process, Manual Triaging, Data Mining Techniques, Text Classification, Data Reduction, Domain Specific Environment

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