DocumentCode :
3658505
Title :
An Implementation of Just-in-Time Fault-Prone Prediction Technique Using Text Classifier
Author :
Keita Mori;Osamu Mizuno
Author_Institution :
Grad. Sch. of Sci. &
Volume :
3
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
609
Lastpage :
612
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.
Keywords :
"Predictive models","Filtering","Data mining","Accuracy","Software maintenance","Databases"
Publisher :
ieee
Conference_Titel :
Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual
Electronic_ISBN :
0730-3157
Type :
conf
DOI :
10.1109/COMPSAC.2015.143
Filename :
7273434
Link To Document :
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