# Improved Query Reformulation for Concept Location using CodeRank and Document Structures

arXiv: https://arxiv.org/abs/1807.04488 · HTML: https://mlsanity.com/paper/1807.04488/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study](https://mlsanity.com/paper/2108.05341/) (2021)
- [Improved IR-based Bug Localization with Intelligent Relevance Feedback](https://mlsanity.com/paper/2501.10542/) (2025)
- [Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow](https://mlsanity.com/paper/2102.00826/) (2021)
- [A Systematic Review of Automated Query Reformulations in Source Code Search](https://mlsanity.com/paper/2108.09646/) (2021)

## Abstract (arXiv)

During software maintenance, developers usually deal with a significant number of software change requests. As a part of this, they often formulate an initial query from the request texts, and then attempt to map the concepts discussed in the request to relevant source code locations in the software system (a.k.a., concept location). Unfortunately, studies suggest that they often perform poorly in choosing the right search terms for a change task. In this paper, we propose a novel technique --ACER-- that takes an initial query, identifies appropriate search terms from the source code using a novel term weight --CodeRank, and then suggests effective reformulation to the initial query by exploiting the source document structures, query quality analysis and machine learning. Experiments with 1,675 baseline queries from eight subject systems report that our technique can improve 71% of the baseline queries which is highly promising. Comparison with five closely related existing techniques in query reformulation not only validates our empirical findings but also demonstrates the superiority of our technique.