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Found 8 papers, 2 papers with code
Natural Language Requirements Testability Measurement Based on Requirement Smells
However, despite the importance of measuring and quantifying requirements testability, no automatic approach for measuring requirements testability has been proposed based on the requirements smells, which are at odds with the requirements testability.
Mitigating Backdoors within Deep Neural Networks in Data-limited Configuration
As the capacity of deep neural networks (DNNs) increases, their need for huge amounts of data significantly grows.
Path Analysis for Effective Fault Localization in Deep Neural Networks
Deep learning has revolutionized various real-world applications, but the quality of Deep Neural Networks (DNNs) remains a concern.
A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications.
A systematic literature review on the code smells datasets and validation mechanisms
The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools.
Learning to predict test effectiveness
Compared with the state-of-the-art coverage prediction models, our models improve MAE, MSE, and an R2-score by 5. 78%, 2. 84%, and 20. 71%, respectively.
An ensemble meta-estimator to predict source code testability
Therefore, testability can be measured based on the coverage and number of test cases provided by a test suite, considering the test budget.
Format-aware Learn&Fuzz: Deep Test Data Generation for Efficient Fuzzing
Our approach generates new test data while distinguishes between data and meta-data that makes it possible to target both the parsing and rendering parts of software under test (SUT).
Software Engineering
