NDLI: Framework for Intelligent Meaningful Test Data Generation model-IMTDG

Content Provider	IEEE Xplore Digital Library
Author	Hameed, S.A. Deraman, A. Hamdan, A.R.
Copyright Year	2000
Description	Author affiliation: Int. Islamic Univ. Malaysia, Gombak, Malaysia (Hameed, S.A.)
Abstract	The generation of actual test data is one of the difficult and expensive parts of applying software testing techniques. Many of the current test data generators focus firstly on the data type and structure, regardless of the meaning, and secondly on developer or database administrator viewpoints, regardless of user concerns. This leads to generating a high amount of meaningless test data, especially when generating non-numeric data, which may not reflect the specification or environment of the population under test, besides the reduction of user confidence in the generated data and in testing at all! In this paper, we propose a framework for an intelligent meaningful test data generation (IMTDG) model with the aim of increasing the users' confidence in software testing. The model uses samples of real data as resource data and a set of efficient generation techniques based on statistical methods, such as permutations, combinations, sampling and statistical distributions. Selection of the most suitable structure and generation technique is based on one of the intelligent soft computing techniques, such as fuzzy logic, neural networks or genetic algorithms. The generated test data is close to the real-world data for testing processes with the ability to simulate real environments.
Starting Page	16
Ending Page	22
File Size	1321893
Page Count	7
File Format	PDF
ISBN	0780363558
DOI	10.1109/TENCON.2000.888380
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-24
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software testing Databases Statistical analysis Sampling methods Statistical distributions Intelligent networks Intelligent structures Computer networks Fuzzy logic Neural networks
Content Type	Text
Resource Type	Article

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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
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