|
| |
VIEW
TOC [PDF
Full-Text (486 KB)] PREV NEXT ABSTRACT
PLUS
You are not currently logged in. Guests
may access abstract/citation records free of charge. Click
here to log in.
You may purchase the PDF full-text
document for up to three (3) downloads within 30 days.
Using regression trees to classify
fault-prone software modules
Khoshgoftaar,
T.M. Allen, E.B. Jianyu
Deng
Dept. of
Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton,
FL;
This paper appears
in: Reliability, IEEE Transactions on
Publication Date: Dec 2002
On page(s): 455- 462
Volume: 51, Issue: 4
ISSN:
0018-9529
Abstract:
Software faults are defects in software modules
that might cause failures. Software developers tend to focus on
faults, because they are closely related to the amount of rework
necessary to prevent future operational software failures. The goal
of this paper is to predict which modules are fault-prone and to do
it early enough in the life cycle to be useful to developers. A
regression tree is an algorithm represented by an abstract tree,
where the response variable is a real quantity. Software modules are
classified as fault-prone or not, by comparing the predicted value
to a threshold. A classification rule is proposed that allows one to
choose a preferred balance between the two types of
misclassification rates. A case study of a very large
telecommunications systems considered software modules to be
fault-prone, if any faults were discovered by customers. Our
research shows that classifying fault-prone modules with regression
trees and the using the classification rule in this paper, resulted
in predictions with satisfactory accuracy and robustness.
VIEW
TOC [PDF
Full-Text (486 KB)] PREV NEXT ABSTRACT
PLUS
| |
