Discrete and combinatorial gravitational search algorithms for test case prioritization and minimization

Regression testing is an essential but expensive activity to re-execute all the test cases every time the software updates. Test case prioritization and minimization reduces the cost and efforts required for retesting by prioritizing the test cases based on their importance and minimizing the redund...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of information technology (Singapore. Online) Ročník 13; číslo 2; s. 817 - 823
Hlavní autoři: Bajaj, Anu, Sangwan, Om Prakash
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Singapore 01.04.2021
Springer Nature B.V
Témata:
Artificial Intelligence
Combinatorial analysis
Computer Imaging
Computer Science
Genetic algorithms
Gravitational constant
Image Processing and Computer Vision
Machine Learning
Model testing
Optimization
Original Research
Pattern Recognition and Graphics
Performance measurement
Redundancy
Search algorithms
Software Engineering
Statistical analysis
Vision
Test case minimization
Gravitational search algorithm
Genetic algorithm
Test case prioritization
Nature-inspired algorithms
Discrete optimization
Regression testing
Search-based software testing
Combinatorial optimization
ISSN:2511-2104, 2511-2112
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Regression testing is an essential but expensive activity to re-execute all the test cases every time the software updates. Test case prioritization and minimization reduces the cost and efforts required for retesting by prioritizing the test cases based on their importance and minimizing the redundancy. Optimization approaches further enhance the effectiveness of these techniques. In this paper, a discrete and combinatorial gravitational search algorithm is proposed to solve the test case prioritization and minimization problems. Furthermore, an improved version is developed using the chaotic map to update the gravitational constant. The proposed algorithms are compared with the most commonly used algorithm, i.e., genetic algorithm. Three subject programs of varying sizes are used for evaluation. Simulation results prove that the proposed algorithms are more efficient and effective than the genetic algorithm for test case prioritization and minimization. Statistical representation via boxplots of APFD and interval plots of minimized suite size performance metrics, confirms that the improved gravitational search algorithm with chaotic gravitational constant has a more squeezed distribution than the standard gravitational search algorithm.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-021-00628-8