QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledge

Accepted Paper at ASE 2016

QUICKAR: Automatic Query Reformulation for Concept Location Using Crowdsourced Knowledge
Mohammad Masudur Rahman and Chanchal K. Roy

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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.

Experimental Data

Subject Systems (2)

• ecf (222)
• eclipse.pde.ui (288)

Baseline Method

• Query: Baseline queries used in the experiment
• QE: Query Effectiveness achieved by baseline queries

QUICKAR

• Query: Queries suggested by QUICKAR
• QE: Query Effectiveness achieved by QUICKAR queries

Please cite our work as

@inproceedings{ase2016masud, 
author = {Rahman, M. M. and Roy, C. K. }, 
title = {{QUICKAR: Automatic Query Reformulation for Concept Location Using Crowdsourced Knowledge}}, 
booktitle = {Proc. ASE}, 
year = {2016}, 
pages = {220--225} }

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Related Projects: ACER, STRICT, and BLIZZARD

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