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Test Suite Optimization Using Machine Learning Techniques: A Comprehensive Study
Software testing is an essential yet costly phase of the software development lifecycle. While
machine learning-based test suite optimization techniques have shown promise in reducing …
machine learning-based test suite optimization techniques have shown promise in reducing …
Test optimization in dnn testing: A survey
This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …
A survey on test input selection and prioritization for deep neural networks
With the breakthrough advancements of deep neural network technology in applications
such as image processing, autonomous driving, and speech recognition, the testing of deep …
such as image processing, autonomous driving, and speech recognition, the testing of deep …
Analytic hierarchy process-based regression test case prioritization technique enhancing the fault detection rate
Regression testing is a testing method conducted to ensure that improvements do not affect
the software's current behavior. Test cases play a significant role in software testing activities …
the software's current behavior. Test cases play a significant role in software testing activities …
Distribution aware testing framework for deep neural networks
The increasing use of deep learning (DL) in safety-critical applications highlights the critical
need for systematic and effective testing to ensure system reliability and quality. In this …
need for systematic and effective testing to ensure system reliability and quality. In this …
Runtime Monitoring DNN-Based Perception: (via the Lens of Formal Methods)
Deep neural networks (DNNs) are instrumental in realizing complex perception systems. As
many of these applications are safety-critical by design, engineering rigor is required to …
many of these applications are safety-critical by design, engineering rigor is required to …
Tpfl: Test input prioritization for deep neural networks based on fault localization
DNN testing is a critical way to guarantee the quality of DNNs. To obtain test oracle
information, DNN testing requires a huge cost to label test inputs, which greatly affects the …
information, DNN testing requires a huge cost to label test inputs, which greatly affects the …
Towards Improving the Quality of Requirement and Testing Process in Agile Software Development: An Empirical Study.
Software testing is a critical phase due to misconceptions about ambiguities in the
requirements during specification, which affect the testing process. Therefore, it is difficult to …
requirements during specification, which affect the testing process. Therefore, it is difficult to …
An exploratory study of history-based test case prioritization techniques on different datasets
In regression testing, Test case prioritization (TCP) is a technique to arrange all the available
test cases. TCP techniques can improve fault detection performance which is measured by …
test cases. TCP techniques can improve fault detection performance which is measured by …
Test Selection for Deep Neural Networks using Meta-Models with Uncertainty Metrics
With the use of Deep Learning (DL) in safety-critical domains, the systematic testing of these
systems has become a critical issue for human life. Due to the data-driven nature of Deep …
systems has become a critical issue for human life. Due to the data-driven nature of Deep …