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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …
in achieving human-level performance on several long-standing tasks. With the broader …
Testing machine learning based systems: a systematic map**
Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …
one or more components that learn how to perform a task from a given data set. The …
Software engineering for AI-based systems: a survey
AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
Machine learning testing: Survey, landscapes and horizons
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
Deephunter: a coverage-guided fuzz testing framework for deep neural networks
The past decade has seen the great potential of applying deep neural network (DNN) based
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …
A software engineering perspective on engineering machine learning systems: State of the art and challenges
Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …
software development, where algorithms are hard-coded by humans, to ML systems …
How does machine learning change software development practices?
Adding an ability for a system to learn inherently adds uncertainty into the system. Given the
rising popularity of incorporating machine learning into systems, we wondered how the …
rising popularity of incorporating machine learning into systems, we wondered how the …
Causality-based neural network repair
Neural networks have had discernible achievements in a wide range of applications. The
wide-spread adoption also raises the concern of their dependability and reliability. Similar to …
wide-spread adoption also raises the concern of their dependability and reliability. Similar to …
Deep learning based software defect prediction
Software systems have become larger and more complex than ever. Such characteristics
make it very challengeable to prevent software defects. Therefore, automatically predicting …
make it very challengeable to prevent software defects. Therefore, automatically predicting …
Deep learning library testing via effective model generation
Deep learning (DL) techniques are rapidly developed and have been widely adopted in
practice. However, similar to traditional software systems, DL systems also contain bugs …
practice. However, similar to traditional software systems, DL systems also contain bugs …