Mountain View
biomedical and healthCAre Data Discovery Index Ecosystem
help Advanced Search
Title: Tomcat Bug-Report      
dateReleased:
02-03-2017
privacy:
information not avaiable
aggregation:
instance of dataset
dateCreated:
02-03-2017
refinement:
raw
ID:
doi:10.5281/ZENODO.268481
creators:
Lam, An Ngoc
availability:
available
types:
other
description:
About the Data This dataset is one of the Datasets donated by An Ngoc Lam. Overview of Data The data is present in 2 files: “Tomcat.xlsx” : A spreadsheet with the bug-ids, commits, its summary, files etc. “Tomcat.xml” : An xml file with more detailed information than the above spreadsheet(detailed files changed). Attribute Information The spreadsheet contains a table with the “bug_id”, “summary”, “description”, “time_reported”, “commit associated”, “status of commit” and “files committed”. The xml contains the above information and additionally the lines associated with the commit. Paper Abstract Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.
accessURL: https://doi.org/10.5281/ZENODO.268481
storedIn:
Zenodo
qualifier:
not compressed
format:
HTML
accessType:
landing page
authentication:
none
authorization:
none
abbreviation:
ZENODO
homePage: https://zenodo.org/
ID:
SCR:004129
name:
ZENODO

Feedback?

If you are having problems using our tools, or if you would just like to send us some feedback, please post your questions on GitHub.