Graph-Based Feature Learning for Cross-Project Software Defect Prediction

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state.However,the collection of fault signals is very difficult and expensive,resulting in the problem of imbalanced training dataset.It will degrade the performance of fault diagnosis methods significantly.To address this problem,an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper.Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph.And the edge connections in the graph depend on the relationship between signals.On the basis,graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery.Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform,and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

An Intrusion Detection Algorithm Based on Feature Graph

With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by various of online malicious behaviors.The aim of an intrusion detection system(IDS)is to detect the network behaviors which are diverse and malicious.Since a conventional firewall cannot detect most of the malicious behaviors,such as malicious network traffic or computer abuse,some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches.However,there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph.In this paper,a novel intrusion detection approach IDBFG(Intrusion Detection Based on Feature Graph)is proposed which first filters normal connections with grid partitions,and then records the patterns of various attacks with a novel graph structure,and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors.The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM(Supprot Vector Machines)and Decision Tree which are trained and tested in original feature space in terms of detection rates,false alarm rates and run time.

Chinese Semantic Parsing Based on Feature Structure with Recursive Directed Graph

It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.

Solving Topological and Geometrical Constraints in Bridge Feature Model

The capacity that computer can solve more complex design problem was gradually increased. Bridge designs need a breakthrough in the current development limitations, and then become more intelligent and integrated. This paper proposes a new parametric and feature-based computer aided design (CAD) models which can represent families of bridge objects, includes knowledge representation, three-dimensional geometric topology relationships. The realization of a family member is found by solving first the geometric constraints, and then the topological constraints. From the geometric solution, constraint equations are constructed. Topology solution is developed by feature dependencies graph between bridge objects. Finally, feature parameters are proposed to drive bridge design with feature parameters. Results from our implementation show that the method can help to facilitate bridge design.