一句话：提出 BLIZZARD，通过判断缺陷报告中的程序实体是否过多并上下文感知地重构查询，提升基于 IR 的缺陷定位。
基于信息检索的缺陷定位在缺陷报告缺少丰富结构化信息时表现不佳，而过多的结构化信息（如堆栈轨迹）也未必总能帮助自动定位。本文提出 BLIZZARD：它先判断缺陷报告（作为查询）中是否存在过多程序实体，再针对查询应用合适的上下文感知重构，并借助有效的信息检索从项目源码中定位有缺陷的实体。作者在 5,139 个缺陷报告上评估，相比基线技术在多个指标上提升 7%–56%、6%–62% 和 6%–62%；与 state-of-the-art 及其变体相比，在两项指标上分别提升 19% 和 20%，并改进了 59% 的噪声查询与 39% 的低质量查询。
Recent findings suggest that Information Retrieval (IR)-based bug localization techniques do not perform well if the bug report lacks rich structured information (e.g., relevant program entity names). Conversely, excessive structured information (e.g., stack traces) in the bug report might not always help the automated localization either. In this paper, we propose a novel technique--BLIZZARD-- that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval. In particular, our technique determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization. Experiments using 5,139 bug reports show that our technique can localize the buggy source documents with 7%--56% higher [email protected], 6%--62% higher [email protected] and 6%--62% higher [email protected] than the baseline technique. Comparison with the state-of-the-art techniques and their variants report that our technique can improve 19% in [email protected] and 20% in [email protected] over the state-of-the-art, and can improve 59% of the noisy queries and 39% of the poor queries.
DOI 原文 ·
@inproceedings{paperbot4654,
title = {Improving IR-based bug localization with context-aware query reformulation},
author = {Mohammad Masudur Rahman and Chanchal K. Roy},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3236065}
}