6,945 research outputs found
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models
Code Completion with Neural Attention and Pointer Networks
Intelligent code completion has become an essential research task to
accelerate modern software development. To facilitate effective code completion
for dynamically-typed programming languages, we apply neural language models by
learning from large codebases, and develop a tailored attention mechanism for
code completion. However, standard neural language models even with attention
mechanism cannot correctly predict the out-of-vocabulary (OoV) words that
restrict the code completion performance. In this paper, inspired by the
prevalence of locally repeated terms in program source code, and the recently
proposed pointer copy mechanism, we propose a pointer mixture network for
better predicting OoV words in code completion. Based on the context, the
pointer mixture network learns to either generate a within-vocabulary word
through an RNN component, or regenerate an OoV word from local context through
a pointer component. Experiments on two benchmarked datasets demonstrate the
effectiveness of our attention mechanism and pointer mixture network on the
code completion task.Comment: Accepted in IJCAI 201
Vibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock
One of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict bearing health. The models are intended to work in tandem with the onboard monitoring sensors as a means of two-way practical validation. The models tested were the Gradient Boosting Machine architecture for scheduled predictions and the Informer neural network architecture for long-term predictions of ongoing routes. The dataset for these models comes from the expansive laboratory record data available at the UTCRS. Ultimately, these machine learning algorithms will enhance freight railcars safety and save rail companies money by allowing for predictive maintenance practices
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