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 …

With the growth of software systems, logs have become an important data to aid system
maintenance. Log-based anomaly detection is one of the most important methods for such …

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 …

There are increasing uses of deep learning (DL) compilers to generate optimized code,
boosting the runtime performance of DL models on specific hardware. Like their traditional …

Deep learning (DL) systems can make our life much easier, and thus are gaining more and
more attention from both academia and industry. Meanwhile, bugs in DL systems can be …

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the
quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is …

Similar to traditional software that is constantly under evolution, deep neural networks need
to evolve upon the rapid growth of test data for continuous enhancement (eg, adapting to …

In large-scale online service systems, software changes are inevitable and frequent. Due to
importing new code or configurations, changes are likely to incur incidents and destroy user …

DL frameworks are the basis of constructing all DL programs and models, and thus their
bugs could lead to the unexpected behaviors of any DL program or model relying on them …

Question answering (QA) software uses information retrieval and natural language
processing techniques to automatically answer questions posed by humans in a natural …