Hybrid Approach for Automated Test Data Generation Article Swipe
Gagan Kumar
,
Vinay Chopra
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.13052/jicts2245-800x.1043
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.13052/jicts2245-800x.1043
Software testing has long been thought to be a good technique to improve the software quality and reliability. Path testing is the most reliable software testing technique and the key method for improving software quality among all testing approaches. On the other hand, test data quality has a big impact on the software testing activity’s ability to detect errors or defects. To solving testing problem, one must locate the entire search space for the relevant input data to encompass the different paths in the testable program. To satisfy path coverage, it is vital test to look at the accumulated test data across the thorough search area. A new approach based on ant colony optimization and negative selection algorithm (HACO-NSA) is presented in this research which overcome the flaws associated with search-based test data by generated automated test data. The optimum path testing objective is to generate appropriate test data to maximise coverage and to enhance the test data’s efficacy, as a result, the test data’s adequacy is validated using a path-based fitness function. In the NSA generation stage, the suggested method alters the new detectors creation using ACO. The proposed approach is evaluated for metrics such as average coverage, average generation, average time, and success rate and comparison has been done with random testing, ant colony optimization and negative selection algorithm Different benchmark programs have been used for object-oriented system. The findings show that the hybrid methodology escalates the coverage percentage and curtail test data size, reduces the redundancy in data and enhances the efficiency. The proposed approach is follows IEEE 829-2008 test documentation in entire testing process.
Related Topics
Concepts
Computer science
Ant colony optimization algorithms
Test data
Reliability engineering
Benchmark (surveying)
Random testing
Software quality
Code coverage
Data mining
Test Management Approach
Software
Redundancy (engineering)
Test suite
Test case
Software system
Algorithm
Machine learning
Software development
Engineering
Software construction
Operating system
Geography
Programming language
Regression analysis
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.13052/jicts2245-800x.1043
- https://journals.riverpublishers.com/index.php/JICTS/article/download/12743/15125
- OA Status
- diamond
- Cited By
- 1
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313213395
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313213395Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.13052/jicts2245-800x.1043Digital Object Identifier
- Title
-
Hybrid Approach for Automated Test Data GenerationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-02Full publication date if available
- Authors
-
Gagan Kumar, Vinay ChopraList of authors in order
- Landing page
-
https://doi.org/10.13052/jicts2245-800x.1043Publisher landing page
- PDF URL
-
https://journals.riverpublishers.com/index.php/JICTS/article/download/12743/15125Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://journals.riverpublishers.com/index.php/JICTS/article/download/12743/15125Direct OA link when available
- Concepts
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Computer science, Ant colony optimization algorithms, Test data, Reliability engineering, Benchmark (surveying), Random testing, Software quality, Code coverage, Data mining, Test Management Approach, Software, Redundancy (engineering), Test suite, Test case, Software system, Algorithm, Machine learning, Software development, Engineering, Software construction, Operating system, Geography, Programming language, Regression analysis, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2132264164, https://openalex.org/W2528041244, https://openalex.org/W2519404962, https://openalex.org/W1979345446, https://openalex.org/W2213891260, https://openalex.org/W2163246860, https://openalex.org/W2777704911, https://openalex.org/W2127470057, https://openalex.org/W2263213757, https://openalex.org/W4237595082, https://openalex.org/W2127174963, https://openalex.org/W3110289350, https://openalex.org/W2404402506, https://openalex.org/W2029132652, https://openalex.org/W2101352341, https://openalex.org/W2325253455, https://openalex.org/W2117863925, https://openalex.org/W2106975767, https://openalex.org/W2188567469, https://openalex.org/W1983019393, https://openalex.org/W1976163299, https://openalex.org/W1971678174, https://openalex.org/W2055639728, https://openalex.org/W3105524585, https://openalex.org/W2529549081, https://openalex.org/W2563281897, https://openalex.org/W1982568211, https://openalex.org/W2557129576, https://openalex.org/W1954404225, https://openalex.org/W1975009952, https://openalex.org/W2012017253, https://openalex.org/W7055464485, https://openalex.org/W2014494924, https://openalex.org/W2155779228, https://openalex.org/W2164755677, https://openalex.org/W1966665997, https://openalex.org/W2546317702, https://openalex.org/W2128705399, https://openalex.org/W2051798585, https://openalex.org/W2159832688, https://openalex.org/W2524651457, https://openalex.org/W186259066, https://openalex.org/W1530490667, https://openalex.org/W2092382400, https://openalex.org/W2137636669, https://openalex.org/W3145133647, https://openalex.org/W2731936366, https://openalex.org/W2140540394, https://openalex.org/W1969065560 |
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