Automated Documentation to Code Traceability Link Recovery and Visualization

Show simple item record

dc.contributor.advisor Hosking, J en
dc.contributor.advisor Grundy, J en
dc.contributor.advisor Amor, R en
dc.contributor.author Chen, Xiaofan en
dc.date.accessioned 2013-03-21T00:53:08Z en
dc.date.issued 2012 en
dc.identifier.uri http://hdl.handle.net/2292/20323 en
dc.description.abstract Documentation written in natural language and source code are two of the major artifacts of a system. Tracking a variety of traceability links between documentation and code assists developers in comprehension, efficient development, and effective management of a system. However, automated traceability systems to date have faced with three major open research challenges. The first challenge is how to extract links with both high precision and high recall. We introduce an approach that combines three supporting techniques, Regular Expression, Key Phrases, and Clustering, with Information Retrieval (IR) models to improve the performance of automated traceability recovery between documents and source code. This combination approach takes advantage of strengths of the three techniques to ameliorate limitations of IR models. Our experimental results show that our approach improves the performance of IR models, increases the precision of retrieved links, and recovers more true links than IR alone. The second challenge is how to establish robust traceability benchmarks to evaluate traceability recovery techniques. We describe an approach and guidelines to enable researchers to establish affordable and robust traceability benchmarks. We have designed rigorous manual identification and verification strategies to determine whether or not a link is correct. We have developed a formula to calculate the probability of errors made in created benchmarks. The analysis of error probability results shows that our approach can build high quality benchmarks and our strategies significantly reduce the error probability in them. The third challenge is how to efficiently visualize links for complex systems because of scalability and visual clutter issues. We present a new approach that combines treemap and hierarchical tree techniques to reduce visual clutter and to allow the visualization of the global structure of traces and a detailed overview of each trace, while still being highly scalable and interactive. The usability evaluation results show that our approach can effectively and efficiently help software developers comprehend, browse, and maintain large numbers of links. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof PhD Thesis - University of Auckland en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ en
dc.title Automated Documentation to Code Traceability Link Recovery and Visualization en
dc.type Thesis en
thesis.degree.grantor The University of Auckland en
thesis.degree.level Doctoral en
thesis.degree.name PhD en
dc.rights.holder Copyright: The Author en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.elements-id 374554 en
pubs.record-created-at-source-date 2013-03-21 en


Files in this item

Find Full text

This item appears in the following Collection(s)

Show simple item record

Share

Search ResearchSpace


Browse

Statistics