Authors :
R.Shankar; Dr. D. Sridhar
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3WfPjv5
DOI :
https://doi.org/10.5281/zenodo.8282823
Abstract :
Continuous Integration (CI) platforms enable
recurrent integration of software variations, creating
software development rapidly and cost-effectively. In
these platforms, integration, and regression testing play
an essential role in Test Case Prioritization (TCP) to
detect the test case order, which enhances specific
objectives like early failure discovery. Currently,
Artificial Intelligence (AI) models have emerged widely to
solve complex software testing problems like integration
and regression testing that create a huge quantity of data
from iterative code commits and test executions. In CI
testing scenarios, AI models comprising machine and
deep learning predictors can be trained by using large
test data to predict test cases and speed up the discovery
of regression faults during code integration. But these
models attain various efficiencies based on the context
and factors of CI testing such as varying time cost or the
size of test execution history utilized to prioritize failing
test cases. Earlier research on TCP using AI models does
not often learn these variables that are crucial for CI
testing. In this article, a comprehensive review of the
different TCP models using deep-learning algorithms
including Reinforcement Learning (RL) is presented to
pay attention to the software testing field. Also, the merits
and demerits of those models for TCP in CI testing are
examined to comprehend the challenges of TCP in CI
testing. According to the observed challenges, possible
solutions are given to enhance the accuracy and stability
of deep learning models in CI testing for TCP.
Keywords :
Software Testing, Continuous Integration Testing, Regression Testing, Test Case Prioritization, Artificial Intelligence, Deep Learning, Reinforcement Learning.
Continuous Integration (CI) platforms enable
recurrent integration of software variations, creating
software development rapidly and cost-effectively. In
these platforms, integration, and regression testing play
an essential role in Test Case Prioritization (TCP) to
detect the test case order, which enhances specific
objectives like early failure discovery. Currently,
Artificial Intelligence (AI) models have emerged widely to
solve complex software testing problems like integration
and regression testing that create a huge quantity of data
from iterative code commits and test executions. In CI
testing scenarios, AI models comprising machine and
deep learning predictors can be trained by using large
test data to predict test cases and speed up the discovery
of regression faults during code integration. But these
models attain various efficiencies based on the context
and factors of CI testing such as varying time cost or the
size of test execution history utilized to prioritize failing
test cases. Earlier research on TCP using AI models does
not often learn these variables that are crucial for CI
testing. In this article, a comprehensive review of the
different TCP models using deep-learning algorithms
including Reinforcement Learning (RL) is presented to
pay attention to the software testing field. Also, the merits
and demerits of those models for TCP in CI testing are
examined to comprehend the challenges of TCP in CI
testing. According to the observed challenges, possible
solutions are given to enhance the accuracy and stability
of deep learning models in CI testing for TCP.
Keywords :
Software Testing, Continuous Integration Testing, Regression Testing, Test Case Prioritization, Artificial Intelligence, Deep Learning, Reinforcement Learning.
