A Statistical Model to Locate Faults at Input Level

Authors:
Wu Ji;Jia Xiao-xia;Liu Chang;Yang Hai-yan;Liu Chao;Jin Mao-zhong
Affiliations:
Beijing University of Aeronautics and Astronautics, Beijing, China;Beijing University of Aeronautics and Astronautics, Beijing, China;Beijing University of Aeronautics and Astronautics, Beijing, China;Beijing University of Aeronautics and Astronautics, Beijing, China;Beijing University of Aeronautics and Astronautics, Beijing, China;Beijing University of Aeronautics and Astronautics, Beijing, China
Venue:
Proceedings of the 19th IEEE international conference on Automated software engineering
Year:
2004

Citing 7
Cited 1

Interprocedural slicing using dependence graphs

ACM Transactions on Programming Languages and Systems (TOPLAS)
The use of program profiling for software maintenance with applications to the year 2000 problem

ESEC '97/FSE-5 Proceedings of the 6th European SOFTWARE ENGINEERING conference held jointly with the 5th ACM SIGSOFT international symposium on Foundations of software engineering
Simplifying failure-inducing input

Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Simplifying and Isolating Failure-Inducing Input

IEEE Transactions on Software Engineering
Visualization of test information to assist fault localization

Proceedings of the 24th International Conference on Software Engineering
ConSIT: A Conditioned Program Slicer

ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
Program Slicing

IEEE Transactions on Software Engineering

Penumbra: automatically identifying failure-relevant inputs using dynamic tainting

Proceedings of the eighteenth international symposium on Software testing and analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a statistical model to locate faults at the input level based on the failure patterns and the success patterns. The model neither needs to be fed with software module, code or trace information, nor does it require re-executing the program. To evaluate the model, precision and recall are adopted as the criteria. Five programs are examined and 17 testing experiments are conducted in which the model gains 0.803 in precision and 0.697 in recall on average.