• Corpus ID: 237635019

Broccoli: Bug localization with the help of text search engines

  title={Broccoli: Bug localization with the help of text search engines},
  author={Benjamin Ledel and Steffen Herbold},
Bug localization is a tedious activity in the bug fixing process in which a software developer tries to locate bugs in the source code described in a bug report. Since this process is time-consuming and sometimes requires additional knowledge about the software project, current literature proposes several information retrieval techniques which can aid the bug localization process. However, recent research questioned the state-of-the-art approaches, since they are barely adopted in practical… 

FineCodeAnalyzer: Multi-Perspective Source Code Analysis Support for Software Developer Through Fine-Granular Level Interactive Code Visualization

This work proposes a tool (called as FineCodeAnalyzer) that supports an interactive source code analysis grounded on structural and historical relations at fine granular-level between the source code elements that outperforms the developers’ self-adopted strategies in locating the code elements.



On the relationship between bug reports and queries for text retrieval-based bug localization

It is shown that highly performing queries can be extracted from the bug report text, in order to make TR effective even without the aforementioned positive biases.

BugLocalizer: integrated tool support for bug localization

A tool named BugLocalizer is developed, which is implemented as a Bugzilla extension and builds upon a recently proposed bug localization technique, which extracts texts from summary and description fields of a bug report and source code files to find potentially buggy files from bug reports.

Are Bug Reports Enough for Text Retrieval-Based Bug Localization?

Analysis of bug report text with and without localization hints using a genetic algorithm to derive a near-optimal query that provides insight into the potential of that bug report for use in TR-based localization shows that in most cases the bug report vocabulary is all the authors need to formulate effective queries, making TR- based bug localization successful without supplementary query expansion.

Version history, similar report, and structure: putting them together for improved bug localization

A new method for locating relevant buggy files that puts together version history, similar reports, and structure is proposed, and a large-scale experiment is performed on four open source projects to localize more than 3,000 bugs.

AmaLgam+: Composing Rich Information Sources for Accurate Bug Localization

A method for locating relevant buggy files that puts together fives sources of information, namely, version history, similar reports, structure, stack traces, and reporter information is proposed, which performs a large‐scale experiment on four open source projects to localize more than 3000 bugs.

Bug Localization Based on Code Change Histories and Bug Reports

BLIA, a statically integrated analysis approach of IR-based bug localization by utilizing texts and stack traces in bug reports, structured information of source files, and source code change histories is proposed.

Improving bug localization using structured information retrieval

This work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice and presents BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available.

Locus: Locating bugs from software changes

An IR-based approach Locus is proposed to locate bugs using software changes, which offer finer granularity than files and provide important contextual clues for bug-fixing, and it is shown that Locus outperforms existing techniques at the source file level localization significantly.

Improving IR-based bug localization with context-aware query reformulation

This paper proposes a novel technique--BLIZZARD-- that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval.

Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports

The results show that BugLocator can effectively locate the files where the bugs should be fixed, and outperforms existing state-of-the-art bug localization methods.