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Personalized defect prediction | IEEE Conference Publication | IEEE Xplore

Personalized defect prediction


Abstract:

Many defect prediction techniques have been proposed. While they often take the author of the code into consideration, none of these techniques build a separate predictio...Show More

Abstract:

Many defect prediction techniques have been proposed. While they often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, causing different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting prediction performance. This paper proposes personalized defect prediction-building a separate prediction model for each developer to predict software defects. As a proof of concept, we apply our personalized defect prediction to classify defects at the file change level. We evaluate our personalized change classification technique on six large software projects written in C and Java-the Linux kernel, PostgreSQL, Xorg, Eclipse, Lucene and Jackrabbit. Our personalized approach can discover up to 155 more bugs than the traditional change classification (210 versus 55) if developers inspect the top 20% lines of code that are predicted buggy. In addition, our approach improves the F1-score by 0.01-0.06 compared to the traditional change classification.
Date of Conference: 11-15 November 2013
Date Added to IEEE Xplore: 02 January 2014
Electronic ISBN:978-1-4799-0215-6
Conference Location: Silicon Valley, CA, USA

References

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