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Toward accurate link between code and software documentation

Science China(Information Sciences) 2018年第5期61卷 72-86页

作者： Yingkui CAO Yanzhen ZOU Yuxiang LUO Bing XIE Junfeng ZHAO Key Laboratory of High Confidence Software Technologies Ministry of Education School of Electronics Engineering and Computer Science Peking University Beida (Binhai) Information Research

Recovering traceability links between source code and software documentation is an important research topic in software maintenance and software reuse. There have been a lot of research efforts in recovering traceability between documentation and code elements(class, interface, method, etc.), mostly based on program analysis. However, there are still a lot of noise links being established in existing work. In this paper, we propose a novel approach to classifying code elements, occurring in a document, into contextual code elements and salient code elements. As a result, we can filter the noise traceability links between a software document and its contextual code elements and get a higher quality link set. Our classifier is trained based on open source project Lucene’s source code and 1899 Stack Overflow answer documents about *** extract code elements from these documents and represent each of these code elements with a 7-dimension feature vector, then we use a decision-tree-based learning model to classify them as salient or not. In the experiments, we get a precision of 70.7% in recognizing the salient code elements of these documents and get12% improvement compared with Rigby’s work. We can filter out 56.5%～69.3% noise traceability links with different thresholds in our classifier. It can improve the quality of traceability links between source code and their related software documents in application.

关键词： traceability link code element software document contextual code element salient codeelement

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