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Data mining is the process of sorting and extraction of hidden predictive information from large databases and it discovering hidden values in your data warehouse.
An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug.
To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage.
In this project, we address the problem of data reduction for bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.
We primarily focus the bug reduction system in this project with the assumption that the communication channel between a developer and the bug reduction is maintained. We have to prevent a redundant bug in the repository. We introduce the novel alternative that provides a significantly-improved bug report. Users dislike the redundancy of the same bug frequently in the bug data and assign an appropriate developer to resolve bug issues. The second approach allows the associated developer to resolve them according to bug classification. This is a tedious assumption since private data can be exposed by either software bugs or configuration errors at the trusted servers or by malicious administrators. Finally, relying on heavy-weight mechanisms to obtain provable redundant bug report.
Why: Problem statement
The feature space has been repeatedly shown to lead to little accuracy loss.
Classifier-based bug prediction techniques are an insufficient performance for practical use and slow prediction times due to a large number of machine-learned features.
These approaches suffer from the large scale and low
text classification technique is used, which means classifying the errors.
A manual developer only has to clear the bugs.
Keywords are provided for searching for particular content by using text classification.
Traditional software analysis is not completely suitable for large-scale and complex data in software repositories.
In traditional software development, new bugs are manually triaged by an expert developer.
How: Solution description
We aim to improve the results of data reduction in bug triage. In this project, We evaluate the reduce the bug data, The scale of a data set and the accuracy of bug triage.
How is it different from competition
Then, we train a binary classifier on bug data sets with extracted attributes and predict the order of applying instance selection and feature selection for a new bug data set.
We are doing data reduction technique on bug data set which will reduce the scale of the data as well as increase the quality of the data. We are using instance selection and feature selection simultaneously with historical bug data.
Who are your customers
Major approaches in the field, along with their technical strengths/weaknesses, followed by a simple runtime performance comparison, and discussion about emerging active learning applications and instance-selection challenges therein.
Project Phases and Schedule
Phase 1: Data collection
Phase 2: Designing
Phase 3: Testing method
Phase 4: Documentation process
Processor - Pentium –III
Speed - 1.1 GHz
RAM - 256 MB (min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA Software Requirements
Operating System - Windows95/98/2000/XP
Language - JAVA JDK 1.3
Development IDE - NETBEANS IDE
Back End - SQL SERVER 2005