QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledge

TL;DR

QUICKAR is a novel method that automatically reformulates software change request queries by leveraging Stack Overflow data, improving query quality for concept location in software maintenance.

Contribution

It introduces a new crowdsourced knowledge-based approach for automatic query reformulation to enhance concept location during software maintenance.

Findings

Improves or maintains query quality in 76% of cases on average.

Uses semantic similarity analysis of adjacent word lists from Stack Overflow.

Outperforms baseline technique in experiments.

Abstract

During maintenance, software developers deal with numerous change requests made by the users of a software system. Studies show that the developers find it challenging to select appropriate search terms from a change request during concept location. In this paper, we propose a novel technique--QUICKAR--that automatically suggests helpful reformulations for a given query by leveraging the crowdsourced knowledge from Stack Overflow. It determines semantic similarity or relevance between any two terms by analyzing their adjacent word lists from the programming questions of Stack Overflow, and then suggests semantically relevant queries for concept location. Experiments using 510 queries from two software systems suggest that our technique can improve or preserve the quality of 76% of the initial queries on average which is promising. Comparison with one baseline technique validates our preliminary findings, and also demonstrates the potential of our technique.