To improve code search, many query expansion (QE) approaches use APIs or crowd knowledge for expanding a query. However, these approaches may sometimes negatively impact the retrieval performance. This is because they can’t distinguish the relevant terms from the irrelevant ones among a large set of candidate expansion terms and expand a query with irrelevant terms. In this paper, we propose QREC, a query reformulation approach with evolving contexts that refer to new/deleted terms and dependent terms during the code evolution. By considering the new terms as the relevant and the deleted terms as the irrelevant, QREC could reformulate a query with appropriate expansion terms. The experimental results show that QREC outperforms the state-of-the-art QE approaches (e.g., CodeHow and QECK) by 9–11% and improves the precision of the code search algorithms IR, Portfolio and VF by up to 37–45%.

中文翻译：

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通过使用不断变化的上下文重新制定查询来增强代码搜索

为了改进代码搜索，许多查询扩展 (QE) 方法使用 API 或人群知识来扩展查询。然而，这些方法有时可能会对检索性能产生负面影响。这是因为他们无法在大量候选扩展术语中区分相关术语和不相关术语，并使用不相关术语扩展查询。在本文中，我们提出了 QREC，这是一种查询重构方法，具有不断发展的上下文，在代码演变过程中引用新/删除的术语和相关术语。通过将新术语视为相关并将删除的术语视为不相关，QREC 可以使用适当的扩展术语重新制定查询。实验结果表明 QREC 优于最先进的 QE 方法（例如，