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Towards Effective Bug Triage with Software Data Reduction Techniques
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Towards Effective Bug Triage with Software Data Reduction Techniques

Category : Data Mining


Sub Category : DOTNET


Project Code : ITDDM07


Project Abstract

TOWARDS EFFECTIVE BUG TRIAGE WITH SOFTWARE

DATA REDUCTION TECHNIQUES

 

ABSTRACT

In this paper, decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. Software companies spend over 45% of cost in dealing with software bugs. A predictable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on bug. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of 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.

 

 

 

 

 

 

 

 

 

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In the existing system guaranteed compute resources always ready to accommodate job execution under reserved instance pricing.

On-demand compute resources, which may delay launching job execution until they are available but on-going execution, will not be aborted, under on-demand pricing.

Bidden compute resources, which abort execution upon price hikes beyond the bidden amount, under spot instance pricing.

PROPOSED CONCEPT:-

In the proposed system aims to present the problem of data reduction for bug triage. This problem aims to augment the data set of bug triage in two aspects, namely to simultaneously reduce the scales of the bug dimension and the word dimension and to improve the accuracy of bug triage.

A combination approach to addressing the problem of data reduction. This can be viewed as an application of instance selection and feature selection in bug repositories.

 

EXISTING TECHNIQUE:-

Text classification techniques

PROPOSED ALGORITHM:-

Data reduction based on FS-IS

TECHNIQUE DEFNITION:-

A bug report is mapped to a document and a related developer is mapped to the label of the document. Then, bug triage is converted into a problem of text classification and is automatically solved with mature text classification techniques

ALGORITHM DEFNITION:-

Leveraging data mining techniques, mining software repositories can uncover interesting information in software repositories and solve real world software problems.

DRAWBACKS:-

Software bugs are predictable and fixing bugs is expensive in software development.

Bug clearance taking too much of time.

Manually developer have to clear the bugs

ADVANTAGES:-

Maintain a Quality in bugs

Automatically clear the bugs

Cost is low
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