NDLI: Using an RBF Neural Network to Locate Program Bugs

Content Provider	IEEE Xplore Digital Library
Author	Wong, W.E. Yan Shi Yu Qi Golden, R.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Texas at Dallas, Dallas, TX (Wong, W.E.; Yan Shi) \|\| Sch. of Behavioral & Brain Sci., Univ. of Texas at Dallas, Dallas, TX (Yu Qi; Golden, R.)
Abstract	We propose an RBF (radial basis function) neural network-based fault localization method to help programmers locate bugs in a more effective way. An RBF neural network with a three-layer feed-forward structure is employed to learn the relationship between the statement coverage of a test case and its corresponding execution result. The trained network is then given as input a set of virtual test cases, each covering only a single statement. The output of the network for each test case is considered to be the suspiciousness of the corresponding statement; a statement with a higher suspiciousness has a higher likelihood of containing a bug. The set of statements ranked in descending order by their suspiciousness are then examined by programmers one by one until a bug is located. Three case studies on different programs (space, grep and make) were conducted with each faulty version having exactly one bug. An additional program gcc was also used to demonstrate the concept of extending the proposed method to programs with multiple bugs. Our experimental data suggest that an RBF neural network-based fault localization method is more effective in locating a program bug (by examining less code before the first faulty statement containing the bug is identified) than another popular method, Tarantula, which also uses the coverage and execution results to compute the suspiciousness of each statement.
Starting Page	27
Ending Page	36
File Size	524946
Page Count	10
File Format	PDF
ISBN	9780769534053
ISSN	10719458
DOI	10.1109/ISSRE.2008.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Computer bugs Testing Biological neural networks Programming profession Neurons Feedforward neural networks Feedforward systems Computer networks Artificial neural networks EXAM score fault localization program debugging RBF (radial basis function) neural network suspiciousness of code successful test failed test
Content Type	Text
Resource Type	Article

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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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