How the Sando Search Tool Recommends Queries
This addresses a specific bottleneck for developers in software engineering, but appears incremental as it builds on existing tools.
The paper tackles the problem of developers struggling to craft effective queries for local code search due to unfamiliarity with their codebase, and demonstrates recommendation techniques in the Sando tool to bridge this gap.
Developers spend a significant amount of time searching their local codebase. To help them search efficiently, researchers have proposed novel tools that apply state-of-the-art information retrieval algorithms to retrieve relevant code snippets from the local codebase. However, these tools still rely on the developer to craft an effective query, which requires that the developer is familiar with the terms contained in the related code snippets. Our empirical data from a state-of-the-art local code search tool, called Sando, suggests that developers are sometimes unacquainted with their local codebase. In order to bridge the gap between developers and their ever-increasing local codebase, in this paper we demonstrate the recommendation techniques integrated in Sando.