Automatic query reformulation is a widely utilized technology for enriching user
requirements and enhancing the outcomes of code search. It can be conceptualized as a …

Code writing is repetitive and predictable, inspiring us to develop various code intelligence
techniques. This survey focuses on code search, that is, to retrieve code that matches a …

We introduce a novel framework named ClarifyGPT, which aims to enhance code
generation by empowering LLMs with the ability to identify ambiguous requirements and ask …

Code search aims to retrieve code snippets from a large-scale codebase, where the
semantics of the searched code match developers' query intent. Code is a low-level …

Asking clarifying questions has become a key element of various conversational systems,
allowing for an effective resolution of ambiguity and uncertainty through natural language …

Large Language Models (LLMs), such as ChatGPT, have demonstrated impressive
capabilities in automatically generating code from provided natural language requirements …

Applying code search models to search through a large-scale codebase can significantly
contribute to developers finding and reusing existing code. Researchers have applied deep …

Developers' API needs should be more pragmatic, such as seeking suggestive, explainable,
and extensible APIs rather than the so-called best result. Existing API search research …

API recommendation methods have evolved from literal and semantic keyword matching to
query expansion and query clarification. The latest query clarification method is knowledge …

The deep‐learning based code search model mainly takes accuracy as the only target for
judging the performance of the model, ignoring the efficiency of code search. This article …