Prediction of the Software Security Defects Based on the Complex Network

The identification of critical nodes in the network is of great significance for the research of the invulnerability and structural stability of complex networks. Most of the existing studies evaluate the importance of nodes in networks from a single structural perspective. Considering the local and global characteristics of the network, an identification algorithm of critical nodes in complex networks based on comprehensive topological importance (CTI) is proposed. The algorithm uses the theory of multiple attribute decision making (MADM) to quantitatively analyze the influence of different attributes on nodes and determine the comprehensive topology importance of nodes, which represents their structural importance in complex networks. Based on two simulation networks and five classical real networks, the proposed algorithm is proved to be superior to DC, LLS, P and WL indexes based on local information and BC indexes based on global information through the change of network efficiency under critical nodes attacking.

Evaluating the importance of nodes in complex networks is an important topic in the research of network characteristics. Its relevant research has a wide range of applications, such as network supervision and rumor control. At present, many methods have been proposed to evaluate the importance of nodes in complex networks, but most of them have the deficiency of one-sided evaluation or too high time complexity. In order to break through the limitations of existing methods, in this paper a novel method of evaluating the importance of complex network nodes is proposed based on Tsallis entropy. This method takes into account both the local and global topological information of the node. It considers the structural hole characteristics and K-shell centrality of the node and fully takes into account the influence of the node itself and its neighboring nodes. To illustrate the effectiveness and applicability of this method, eight real networks are selected from different fields and five existing methods of evaluating node importance are used as comparison methods. On this basis, the monotonicity index, SIR (susceptible-infectious-recovered) model, and Kendall correlation coefficient are used to illustrate the superiority of this method and the relationship among different methods. Experimental results show that this method can effectively and accurately evaluate the importance of nodes in complex networks, distinguish the importance of different nodes significantly, and can show good accuracy of evaluating the node importance under different proportions of nodes. In addition, the time complexity of this method is <inline-formula><tex-math id="M2">\begin{document}$ O({n^2}) $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21-20210979_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21-20210979_M2.png"/></alternatives></inline-formula>, which is suitable for large-scale complex networks.

Accurate measurement of important nodes in complex networks has great practical and theoretical significance. Mining important nodes should not only consider the core nodes, but also take into account the locations of the nodes in the network. Despite much research on assessing important nodes, the importance of nodes in the structural holes is still easily ignored. Therefore, a local measuring method is proposed, which evaluates the nodes importance by the total constraints caused by the lack of primary structural holes and secondary structural holes around the nodes. This method simultaneously considers both the centrality and the bridging property of the nodes' first-order and second-order neighbours. Further to prove the accuracy of the method TCM, we carry out deliberate attack simulations through selective deletion in a certain proportion of network nodes. Then, we calculate the decreased ratio of network efficiency in the before-and-after attacks. Experiment results show that the average effect of the TCM in four real networks are improved by 50.64% and 14.92% compared to the clustering coefficient index and the k-shell decomposition method, respectively. Obviously, the TCM is more accurate to mine important nodes than other two methods, and it is suitable for quantitative analysis in large-scale networks.

Software defects and quality assurance are crucial aspects of software development that should be considered during the software development cycle. To ensure high-quality software, it is essential to have a robust quality assurance process in place. System reliability and quality are very key components that must be considered during software development, and this can only be achieved when software undergoes a thorough test process for errors, anomalies, defects, omissions, and bugs. Early software defect prediction and detection play an essential role in ensuring the reliability and quality of software systems, ensuring that software companies discover errors or defects early enough and allocate more resources to defect-prone modules. This study proposes the development of an enhanced classifier model for software defect prediction and detection. The aim is to harness the collective intelligence of selected base classifiers like Support Vector Machine, Logistic regression, Decision Trees, Random Forest, AdaBoost, Gradient Boosting, K-Nearest Neighbor, GaussianNB, and Multi-Layer Perception to improve accuracy, robustness, and generalization in identifying potential defects using a soft voting ensemble technique. The ensemble model leveraged the confidence probability of the soft voting technique and the generalization advantage of cross-validation leading to a more robust and dynamic model. The performance of the model with existing classifiers was evaluated using accuracy, F1 score, Precision, and area under the ROC curve (ROC- AUC) as the evaluation metrics. The results of the experiment revealed that the Proposed Classifier produced an overall Accuracy rate of 93%, and ROC AUC of 98%. The results demonstrate the effectiveness of our enhanced ensemble classifier in software defect detection and prediction. By harnessing the strengths of diverse base classifiers, our approach provides a robust and adaptive solution to the challenges of early detection and mitigating defects in software systems. This research contributes to the advancement of reliable software development practices and lays the foundation for future enhancements in ensemble-based defect detection methodologies.

Both hardware and software of computer systems are subject to faults. However, traditional methods, ignoring the relationship between software fault and hardware fault, are ineffective to diagnose complex faults between software and hardware. On the basis of defining the interactive effect to describe the process of the interactive software and hardware fault, this paper present a new matrix-oriented negative selection algorithm to detect faults. Furthermore, the row vector distance and matrix distance are constructed to measure elements between the self set and detector set. The experiment on a temperature control system indicates that the proposed algorithm has good fault detection ability, and the method is applicable to diagnose interactive software and hardware faults with small samples.

Influential nodes refer to the ability of a node to spread information in complex networks. Identifying influential nodes is an important problem in complex networks which plays a key role in many applications such as rumor controlling, virus spreading, viral market advertising, research paper views, and citations. Basic measures like degree centrality, betweenness centrality, closeness centrality are identifying influential nodes but they are incapable of largescale networks due to time complexity issues. Chen et al. [1] proposed semi-local centrality, which is reducing computation complexity and finding influential nodes in the network. Recently Yang et al. 2020 [2] proposed a novel centrality measure based on degree and clustering coefficient for identifying the influential nodes. Sanjay et al. 2020 [3] gave voterank and neighborhood coreness-based algorithms for finding the influenced nodes in the network. Zhiwei et al. 2019 [4] considered the average shortest path to discover the influenced node in the network. These are the few recent local,global and mixed centralities. In this paper, we show a broad view of recent methods for finding influential nodes in complex networks. It also analyzes the new challenges and limitations for a better understanding of each method in detail. The experimental results based on these methods show better performance compared with existing basic centrality measures.

Applying effective methods to identify important nodes in a large and complex network is highly invaluable. Recently, in a large and complex network, finding a powerful node in the community to spread information throughout the network is the concern of many researchers. In this paper, to identify powerful nodes in a large and complex network, community-based important node (CBIN) method, which reflects on the community structure of the network and degree of neighbor nodes is proposed as a metrics. CBIN considers a random walk from a node to each community and degree of neighbor nodes. Then, CBIN describes how many influential friends the node has and how the node is essential to connect two or more than two communities of the network. The performance of CBIN is evaluated by Susceptible-Infected (SI) model. Simulation results on a real network show that the proposed method performs better than the existing methods to spread information in the network.

Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China

Proceedings of the association for the prevention of steam boiler explosions, Manchester

Software defect detection is particularly important for modern society, as it is a crucial step in ensuring the quality and reliability of software systems. With the emergence of artificial intelligence (AI), research in software defect detection has evolved from traditional methods to more complex approaches that utilize deep learning and large language models (LLMs). The advent of LLMs has fundamentally changed the paradigm of software development and defect detection, bringing new challenges and confusion to the field of software defect prediction research. To address these issues, we compare software defect detection methods based on traditional techniques, deep learning approaches, and LLMs through a literature review. We analyze the changes brought about by the introduction of LLMs to software development and propose new insights. Additionally, we examine the progress and trends in software defect prediction to provide inspiration for subsequent research.

Ranking influential nodes in complex network using edge weight degree based shell decomposition

Software defect detection is a critical research topic in the field of software engineering, aiming to identify potential defects during the development process to improve software quality and reduce maintenance costs. This study proposes a novel feature selection and defect prediction classification algorithm based on a multi-strategy enhanced Parrot Optimization (PO) algorithm. Firstly, to address the limitations of the original Parrot Optimization algorithm, such as strong dependence on the initial population, premature convergence, and insufficient global search capability, this article develops a multi-strategy enhanced Parrot Optimization algorithm (MEPO). Experiments conducted on eight benchmark test functions validate the superior performance of MEPO in terms of convergence speed and solution accuracy. Secondly, to mitigate the adverse impact of irrelevant features on model performance in traditional software defect prediction methods, this study introduces a binary multi-strategy enhanced Parrot Optimization algorithm (BMEPO) for optimizing feature selection. Comparative experiments demonstrate that BMEPO exhibits stronger competitiveness in feature selection quality and classification performance compared to advanced feature selection algorithms. Finally, to further enhance the classification performance of defect prediction, a heterogeneous data stacking ensemble learning algorithm (HEDSE) based on feature selection is proposed. Experimental evaluations on 16 open-source software defect datasets indicate that the proposed HEDSE outperforms existing methods, providing a novel and effective solution for software defect prediction. The proposed approaches in this study hold significant practical value, particularly in improving software quality, optimizing testing resource allocation, and reducing maintenance costs, offering broad potential for application in real-world software engineering scenarios.

Symmetries, due to their fundamental importance to dynamical processes on networks, have attracted a great deal of current research. Finding all symmetric nodes in large complex networks typically relies on automorphism groups from algebraic-group theory, which are solvable in quasipolynomial time. We articulate a conceptually appealing and computationally extremely efficient approach to finding and characterizing all symmetric nodes by introducing a structural position vector (SPV) for each node in networks. We establish the mathematical result that symmetric nodes must have the same SPV value and demonstrate, using six representative complex networks from the real world, that all symmetric nodes in these networks can be found in linear time. Furthermore, the SPVs not only characterize the similarity of nodes but also quantify the nodal influences in propagation dynamics. A caveat is that the proved mathematical result relating the SPV values to nodal symmetries is not sufficient; i.e., nodes having the same SPV values may not be symmetric, which arises in regular networks or networks with a dominant regular component. We point out with an analysis that this caveat is, in fact, shared by the known existing approaches to finding symmetric nodes in the literature. We further argue, with the aid of a mathematical analysis, that our SPV method is generally effective for finding the symmetric nodes in real-world networks that typically do not have a dominant regular component. Our SPV-based framework, therefore, provides a physically intuitive and computationally efficient way to uncover, understand, and exploit symmetric structures in complex networks arising from real-world applications.

Link prediction is usually studied in unweighted networks, while ignoring the importance of weights in complex networks. In this paper, an algorithm for link prediction with link weight assignments is proposed for unweighted networks. First, weights of links are assigned based on the node degree and clustering coefficient, which describe the closeness of two nodes in complex networks; second, the weighted form of similarity index instead of the unweighted form are applied for link prediction; finally, we compare the performance of link predicitions in networks with or without weight assignments. Experiments on four commonly used complex network data sets have been conducted, and the results show that weights of links play an important role in the complex networks, and by assigning weights to links accordingly the algorithm proposed can improve the link prediction performance.

How to identify influential nodes in social networks is of theoretical significance, which relates to how to prevent epidemic spreading or cascading failure, how to accelerate information diffusion, and so on. In this letter, we make an attempt to find effective multiple spreaders in complex networks by generalizing the idea of the coloring problem in graph theory to complex networks. In our method, each node in a network is colored by one kind of color and nodes with the same color are sorted into an independent set. Then, for a given centrality descriptor, the nodes with the highest centrality in an independent set are chosen as multiple spreaders. Comparing this approach with the traditional method, in which nodes with the highest centrality from the entire network perspective are chosen, we find that our method is more effective in accelerating the spreading process and maximizing the spreading coverage than the traditional method, no matter in network models or in real social networks. Moreover, the low computational complexity of the coloring algorithm guarantees the potential applications of our method.