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Mutation faults are the core of mutation testing and have been widely used in many other software testing and debugging tasks. Hence, constructing high-quality mutation faults is critical. There are many traditional mutation techniques that construct ...
Code embedding, as an emerging paradigm for source code analysis, has attracted much attention over the past few years. It aims to represent code semantics through distributed vector representations, which can be used to support a variety of program ...
Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, such approaches do not capitalize ...
Detecting bugs in deep learning software at the architecture level provides additional benefits that detecting bugs at the model level does not provide. This paper makes the first attempt to conduct static analysis for detecting numerical bugs at the ...
Deep learning is being incorporated in many modern software systems. Deep learning approaches train a deep neural network (DNN) model using training examples, and then use the DNN model for prediction. While the structure of a DNN model as layers is ...
Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web ...
Course instructors need to be able to identify students in need of assistance as early in the course as possible. Recent work has suggested that machine learning approaches applied to snapshots of small programming exercises may be an effective solution ...