| Abstract: | The existing query expansion(QE) methods cannot find the most users-requested source code version at times due to the over-expansion resulting from noises. To solve this problem, we propose a QE method based on evolving contexts(EC) that are added/deleted terms and their dependent terms during code evolution. On expanding a query, we appended the added terms as relevant terms, and excluded the deleted terms as noisy terms. We also developed a QE-integrating framework based on the Support Vector Machine(SVM) Ranking, called QESR, to simultaneously integrate multiple QE methods. Our experiment shows that QESR outperforms the state-of-the-art QE methods CodeHow and Query Expansion based on Crowd Knowledge(QECK) by 13%-16% in terms of precision when the first query result is inspected. |
