CNN Based Severity Prediction of Bug Reports

Locomotion mode recognition has been shown to substantially contribute to the precise control of robotic lower-limb prostheses under different walking conditions. In this study, we proposed a temporal convolutional capsule network (TCCN) which integrates the spatial-temporal-based, dilation-convolution-based, dyna- mic routing and vector-based features for recognizing locomotion mode recognition with small data rather than big-data-based neural networks for robotic prostheses. TCCN proposed in this study has four characteristics, which extracts the (1) spatial-temporal information in the data and then makes (2) dilated convolution to deal with small data, and uses (3) dynamic routing, which produces some similarities to the human brain to process the data as a (4) vector, which is different from other scalar-based networks, such as convolutional neural network (CNN). By comparison with a traditional machine learning, e.g., support vector machine(SVM) and big-data-driven neural networks, e.g., CNN, recurrent neural network(RNN), temporal convolutional network(TCN) and capsule network(CN). The accuracy of TCCN is 4.1% higher than CNN under 5-fold cross-validation of three-locomotion-mode and 5.2% higher under the 5-fold cross-validation of five-locomotion modes. The main confusion we found appears in the transition state. The results indicate that TCCN may handle small data balancing global and local information which is closer to the way how the human brain works, and the capsule layer allows for better processing vector information and retains not only magnitude information, but also direction information.

Accurate load forecasting is essential for ensuring safe, stable, and economical operation of energy internet. Temporal convolutional networks (TCNs) have demonstrated superior performance, when compared to recurrent neural network models, since their introduction in electrical load forecasting. However, the current TCN‐based models are unable to obtain a large receptive field and strong long‐time feature extraction capability owing to the specific kernel size of the 1D convolution structure. This paper proposes a temporal inception convolutional network based on multi‐head attention (TICN‐Att) for ultra‐short‐term load prediction. By introducing an inception structure into the TCN, the proposed model can extract multi‐dimensional information from the input features, through the multiple hidden convolutional kernels of different scales, without stacking layers depth‐wise. Simultaneously, by introducing a multi‐head attention mechanism, the TICN‐Att model has a long time‐dependent extraction capability, similar to that of the long short‐term memory network models. The generalization and validity of the model are tested using the global energy forecasting competition (GEFCOM2014) dataset, electrical load data of a city in Jiangsu (China), and PJM power system dataset. The experimental results demonstrated that the proposed model has the best prediction effect, compared to the other state‐of‐the‐art models.

In addressing the problem of low prediction accuracy in the remaining useful life (RUL) prediction of rolling bearings, caused by noise interference and insufficient extraction of sensitive features by deep learning models, this paper presents a life prediction method based on signal reconstruction and dual-channel network fusion. First, addressing the issue of extracting weak features from rolling bearing vibration signals, an optimized combination of variational mode decomposition (VMD) and Teager–Kaiser energy operator (TKEO) for signal reconstruction is proposed. TKEO is used to track the energy of high-frequency impulses in the original signal. The sparrow search algorithm is employed for optimizing VMD to perform high-frequency filtering, resulting in an optimized impulse energy signal. This signal is then multiplied with the original signal to enhance weak impulse features under noisy background. Next, a dual-channel network model for RUL prediction is constructed based on temporal convolutional network (TCN) and convolutional neural networks (CNNs). The one-dimensional time-series signal obtained after reconstruction and denoising serves as the input for the TCN network channel. Simultaneously, the signal obtained after reconstruction and denoising undergoes synchrosqueezed wavelet transforms to generate a two-dimensional time–frequency representation as input for the CNN network channel. This setup allows TCN and CNN to respectively extract temporal features from the vibration signal and time–frequency features from the spectrogram. By incorporating self-attention mechanisms, internal correlations between different features are fully explored, thereby enhancing prediction accuracy.

The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

Recurrent neural network (RNN) has been widely used for sequential learning which has achieved a great success in different tasks. The temporal convolutional network (TCN), a variant of one-dimensional convolutional neural network (CNN), was also developed for sequential learning in presence of sequence data. RNN and TCN typically captures long-term and short-term features in temporal or spatial domain, respectively. This paper presents a new sequential learning, called the convolutional recurrent network (CRN), which fulfills TCN as an encoder and RNN as a decoder so that the global semantics as well as the local dependencies are simultaneously characterized from sequence data. To facilitate the interpretation and robustness in neural models, we further develop the stochastic modeling for CRN based on variational inference. The merits of CNN and RNN are then incorporated in inference of latent space which sufficiently produces a generative model for sequential prediction. Experiments on language model shows the effectiveness of stochastic CRN when compared with the other sequential machines.

Multivariate time-series forecasting is one of the crucial and persistent challenges in time-series forecasting tasks. As a kind of data with multivariate correlation and volatility, multivariate time series impose highly nonlinear time characteristics on the forecasting model. In this paper, a new multivariate time-series forecasting model, multivariate temporal convolutional attention network (MTCAN), based on a self-attentive mechanism is proposed. MTCAN is based on the Convolution Neural Network (CNN) model, using 1D dilated convolution as the basic unit to construct asymmetric blocks, and then, the feature extraction is performed by the self-attention mechanism to finally obtain the prediction results. The input and output lengths of this network can be determined flexibly. The validation of the method is carried out with three different multivariate time-series datasets. The reliability and accuracy of the prediction results are compared with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Temporal Convolutional Network (TCN). The prediction results show that the model proposed in this paper has significantly improved prediction accuracy and generalization.

Research on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. These heterogeneous sensors deliver information on various aspects of nature, motion and orientation, offering a richer set of features for more accurate representations of the activities. Hence, a deep learning approach that amalgamates long short-term memory (LSTM) in temporal convolutional network (TCN) is proposed. We use independent temporal convolutional networks, coined as temporal convolutional streams, to independently analyse the temporal data of each sensing modality. We name this architecture multi-stream TC-LSTM. The performance of multi-stream TC-LSTM is assessed on the self-collected elderly activity database. Empirical results exhibit that multi-stream TC-LSTM outperforms the existing machine learning and deep learning models, with an F1 score of 98.3 %

The accuracy of short-term electricity price forecasting has profound influence on the business decisions made by the participants in the electricity market. Thus, we propose a short-term electricity price combination prediction model based on Complete Ensemble Empirical Mode Decomposition(CEEMD) as well as Temporal Convolutional Attention-based Network(TCAN). First, we defined the correlations among the features according to the Pearson Correlation Analysis and selected the desired features for our model. We then defined the single prediction period to be 24 hours based on the Unit Root test. In addition, we decomposed and denoised the data based on CEEMD and built the attention mechanism into the temporal convolution network. Using the expansion cascade network to extract the relationship between the inputs, combined with the self-attention mechanism to extract the internal information and learn the position dependence, effectively improve the model training speed and prediction accuracy. In order to verify the validity of the model, the actual electricity price data of DK1 in the Nordic electricity market from April to May 2020 were forecasted. The results show that the proposed method can effectively improve the accuracy and reliability of electricity price forecasting, and can provide reliable decision-making basis.

Data in time series format, such as biological signals from medical sensors or machine signals from sensors in industrial environments are rich sources of information that can give crucial insights on the present and future condition of a person or machine. The task of predicting future values of time series has been initially approached with simple machine learning methods, and lately with deep learning. Two models that have shown good performance in this task are the temporal convolutional network and the attention module. However, despite the promising results of deep learning methods, their black-box nature makes them unsuitable for real-world applications where the predictions need to be explainable in order to be trusted. In this paper we propose an architecture comprised of a temporal convolutional network with an attention mechanism that makes predictions while presenting the timesteps of the input that were most influential for future outputs. We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models. We then go one step further and we combine our configuration with various machine learning methods on top, creating a pipeline that achieves interpretability both across timesteps and input features. We use it to forecast a different variable from one of the above datasets and we study how the accuracy is affected compared to the original black-box approach.

The presence of anomalies in time series, i.e., data points that deviate from the expected behavior, can be a symptom of an incoming failure that may lead to costly repair. Thus, the detection of anomalous data by means of diagnostic methodologies can both reduce maintenance actions and asset unscheduled stops. To tackle this challenge, for the first time in the literature, we exploit the capabilities of Convolutional Neural Networks (CNNs), by feeding them with images obtained from multivariate time series data, transformed by means of two different approaches, namely, Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF). Two CNN architectures are investigated, i.e., VGG-19 and SqueezeNet. The performance of both CNNs fed with images is compared to that of i) a Temporal Convolutional Network (TCN) fed with time series data and ii) a Support Vector Machine (SVM) model. In this paper, we present the comprehensive framework, which starts from time series transformation, goes through CNN development and ends with anomaly detection. The framework is applied to field data taken during normal operation of ten SGT-800 gas turbines, operated in combined cycle, running at base load, and located in two different regions. The normal data covers 150 days of operation. Spike faults are implanted in two out of the twenty available measured variables, i.e., compressor discharge temperature and compressor discharge pressure, by considering nine combinations of maximum fault magnitude and number of implanted spikes in each time series. The analyses carried out in this paper demonstrate that both CNNs fed with images achieve significantly higher classification accuracy than both a TCN model fed with time series data and an SVM model. Moreover, the MTF method always proves more robust than GASF method, and also allows higher accuracy values, in the range from 0.85 to 0.99.

Anomalies in time series can be a symptom of an incoming failure. Thus, the detection of anomalous data can both reduce maintenance actions and asset unscheduled stops. To tackle this challenge, we exploit the capabilities of convolutional neural networks (CNNs), fed with images obtained from multivariate time series data, transformed by means of two different approaches. Two CNN architectures are investigated, i.e., VGG-19 and SqueezeNet. The performance of both CNNs fed with images is compared to that of (i) a temporal convolutional network (TCN) fed with time series data and (ii) a support vector machine (SVM) model. In this paper, we present the comprehensive framework, which starts from time series transformation, goes through CNN development and ends with anomaly detection. The framework is applied to field data taken during normal operation of ten SGT-800 gas turbines, located in two different regions. The normal data covers 150 days of operation. Spike faults are implanted in two out of the 20 available measured variables, i.e., compressor discharge temperature and compressor discharge pressure, by considering different combinations of maximum fault magnitude and number of implanted spikes in each time series. The results demonstrate that both CNNs fed with images achieve significantly higher classification accuracy than both a TCN model fed with time series data and an SVM model. Moreover, the Markov transition field (MTF) method always proves more robust than Gramian angular summation field (GASF) method, and also allows higher accuracy values, in the range from 0.85 to 0.99.

A bug-tracking system such as Bugzilla contains bug reports (BRs) collected from various sources such as development teams, testing teams, and end users. When bug reporters submit bug reports to a bug-tracking system, the bug reporters need to label the bug reports as security bug reports (SBRs) or not, to indicate whether the involved bugs are security problems. These SBRs generally deserve higher priority in bug fixing than not-security bug reports (NSBRs). However, in the bug-reporting process, bug reporters often mislabel SBRs as NSBRs partly due to lack of security domain knowledge. This mislabeling could cause serious damage to software-system stakeholders due to the induced delay of identifying and fixing the involved security bugs. To address this important issue, we developed a new approach that applies text mining on natural-language descriptions of BRs to train a statistical model on already manually-labeled BRs to identify SBRs that are manually-mislabeled as NSBRs. Security engineers can use the model to automate the classification of BRs from large bug databases to reduce the time that they spend on searching for SBRs. We evaluated the model's predictions on a large Cisco software system with over ten million source lines of code. Among a sample of BRs that Cisco bug reporters manually labeled as NSBRs in bug reporting, our model successfully classified a high percentage (78%) of the SBRs as verified by Cisco security engineers, and predicted their classification as SBRs with a probability of at least 0.98.

Bug reporting is essentially an uncoordinated process. The same bugs could be repeatedly reported because users or testers are unaware of previously reported bugs. As a result, extra time could be spent on bug triaging and fixing. In order to reduce redundant effort, it is important to provide bug reporters with the ability to search for previously reported bugs. The search functions provided by the existing bug tracking systems are using relatively simple ranking functions, which often produce unsatisfactory results. In this paper, we adopt Ranking SVM, a Learning to Rank technique to construct a ranking model for effective bug report search. We also propose to use the knowledge of Wikipedia to discover the semantic relations among words and documents. Given a user query, the constructed ranking model can search for relevant bug reports in a bug tracking system. Unlike related works on duplicate bug report detection, our approach retrieves existing bug reports based on short user queries, before the complete bug report is submitted. We perform evaluations on more than 16,340 Eclipse and Mozilla bug reports. The evaluation results show that the proposed approach can achieve better search results than the existing search functions provided by Bugzilla and Lucene. We believe our work can help users and testers locate potential relevant bug reports more precisely.

Gesture recognition as a natural, convenient and recognizable way has been received more and more attention on human–machine interaction (HMI) recently. However, visual-based gesture recognition methods are often restricted by environments and classical wearable device-based strategies are suffered from relatively low accuracy or the complicated structures. In this study, we first design a low-cost and efficient data glove with simple hardware structure to capture finger movement and bending simultaneously. Second, a novel dynamic hand gesture recognition algorithm (DGDL-GR) is proposed to recognize human dynamic sign language, in which a fusion model of convolutional neural network (fCNN) and generic temporal convolutional network (TCN) is fully utilized. The fCNN (fusion of 1-D CNN and 2-D CNN) is proposed to extract time-domain features of finger resistance movement and spatial domain features of finger resistance bending simultaneously. Moreover, due to the superiorities of TCN in sequence modeling task, this work proposes a novel hand gesture recognition method based on the TCN, which includes causal convolution, dilation convolution, and a residual network with appropriate layers. Both long- and short-time dependencies of the hand gesture features are deeply mined and classified in the end. Results of extensive experiments have demonstrated that the proposed DGDL-GR algorithm outperforms many state-of-the-art algorithms on the measure of accuracy, F1 score, precision score, and recall score with the real-world dataset. Moreover, the number of residual blocks and some key hyperparameters of the proposed DGDL-GR algorithm has been studied thoroughly in this work.

Bug reporting is an uncoordinated process that is often the cause of redundant workload in triaging and fixing bugs due to many duplicated bug reports. Furthermore, quite often, same bugs are repeatedly reported as users or testers are unaware of whether they have been reported from the search query results. In order to reduce both the users and developers' efforts, the quality of search in a bug tracking system is crucial. However, all existing search functions in a bug tracking system produce results with undesired relevance and ranking. Hence, it is essential to provide an effective search function to any bug tracking system.Learning to rank (LTR) is a supervised machine learning technique that is used to construct a ranking model from training data. We propose a novel approach by using LTR to search for potentially related bug reports in a bug tracking system. Our method uses a set of proposed features of bug reports and queries. A preliminary evaluation shows that our approach can enhance the quality of searching for similar bug reports, therefore, relieving the burden of developers in dealing with duplicate bug reports.