NDLI: An Implementation of Just-in-Time Fault-Prone Prediction Technique Using Text Classifier

Content Provider	IEEE Xplore Digital Library
Author	Mori, K. Mizuno, O.
Copyright Year	2015
Description	Author affiliation: Grad. Sch. of Sci. & Technol., Kyoto Inst. of Technol., Kyoto, Japan (Mori, K.; Mizuno, O.)
Abstract	Since the fault prediction is an important technique to help allocating software maintenance effort, much research on fault prediction has been proposed so far. The goal of these studies is applying their prediction technique to actual software development. In this paper, we implemented a prototype fault-prone module prediction tool using a text-filtering based technique named "Fault-Prone Filtering". Our tool aims to show the result of fault prediction for each change (i.e., Commits) as a probability that a source code file to be faulty. The result is shown on a Web page and easy to track the histories of prediction. A case study performed on three open source projects shows that our tool could detect 90 percent of the actual fault modules (i.e., The recall of 0.9) with the accuracy of 0.67 and the precision of 0.63 on average.
Starting Page	609
Ending Page	612
File Size	1069888
Page Count	4
File Format	PDF
ISSN	07303157
e-ISBN	9781467365642
DOI	10.1109/COMPSAC.2015.143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Filtering Data mining Accuracy Software maintenance Databases spam filter software development support tool fault prediction software maintenance mining software repository machine learning
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Spam filter based approach for finding fault-prone software modules (2007)

Prediction of fault-prone modules using a text filtering based metric.

Fault-Prone Filtering: Detection of Fault-Prone Modules Using Spam Filtering Technique

Training on errors experiment to detect fault-prone software modules by spam filter

Email Spam Filtering using Supervised Machine Learning Techniques

Fault-prone module detection using large-scale text features based on spam filtering

Spam mail filtering technique using different decision tree classifiers through data mining approach- a comparative performance analysis.

Designing spam model- classification analysis using decision trees.

A large-scale empirical study of just-in-time quality assurance

An Implementation of Just-in-Time Fault-Prone Prediction Technique Using Text Classifier

Similar Documents

Spam filter based approach for finding fault-prone software modules (2007)

Prediction of fault-prone modules using a text filtering based metric.

Fault-Prone Filtering: Detection of Fault-Prone Modules Using Spam Filtering Technique

Training on errors experiment to detect fault-prone software modules by spam filter

Email Spam Filtering using Supervised Machine Learning Techniques

Fault-prone module detection using large-scale text features based on spam filtering

Spam mail filtering technique using different decision tree classifiers through data mining approach- a comparative performance analysis.

Designing spam model- classification analysis using decision trees.

A large-scale empirical study of just-in-time quality assurance

An Implementation of Just-in-Time Fault-Prone Prediction Technique Using Text Classifier