Autoodc: Automated generation of orthogonal defect classifications

influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human intensive and requires experts ' knowledge of both ODC and system domains. This paper presents AutoODC, an approach and tool for automating ODC classification by casting it as a supervised text classification problem. Rather than merely apply the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts ' ODC experience and domain knowledge into the learning process via proposing a novel Relevance Annotation Framework. We evaluated AutoODC on an industrial defect report from the social network domain. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves an overall accuracy of 80.2 % when using manual classifications as a basis of comparison. Keywords-Orthogonal Defect Classification (ODC); text classification; natural language processing I