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Context: Security bug reports (SBRs) usually contain security-related vulnerabilities in software products, which could be exploited by malicious attackers. Hence, it is important to identify SBRs quickly and accurately among bug reports (BRs) that have been disclosed in bug tracking systems. Although a few methods have been already proposed for the detection of SBRs, challenging issues still remain due to noisy samples, class imbalance and data scarcity. Object: This motivates us to reveal the potential challenges faced by the state-of-the-art SBRs prediction methods from the viewpoint of data filtering and representation. Furthermore, the purpose of this paper is also to provide a general framework and new solutions to solve these problems. Method: In this study, we propose a novel approach LTRWES that incorporates learning to rank and word embedding into the identification of SBRs. Unlike previous keyword-based approaches, LTRWES is a content-based data filtering and representation framework that has several desirable properties not shared in other methods. Firstly, it exploits ranking model to efficiently filter non-security bug reports (NSBRs) that have higher content similarity with respect to SBRs. Secondly, it applies word embedding technology to transform the rest of NSBRs, together with SBRs, into low-dimensional real-value vectors. Result: Experiment results on benchmark and large real-world datasets show that our proposed method outperforms the state-of-the-art method. Conclusion: Overall, the LTRWES is valid with high performance. It will help security engineers to identify SBRs from thousands of NSBRs more accurately than existing algorithms. Therefore, this will positively encourage the research and development of the content-based methods for security bug report detection.
Yuan Jiang; Pengcheng Lu; Xiaohong Su; Tiantian Wang. LTRWES: A new framework for security bug report detection. Information and Software Technology 2020, 124, 106314 .
AMA StyleYuan Jiang, Pengcheng Lu, Xiaohong Su, Tiantian Wang. LTRWES: A new framework for security bug report detection. Information and Software Technology. 2020; 124 ():106314.
Chicago/Turabian StyleYuan Jiang; Pengcheng Lu; Xiaohong Su; Tiantian Wang. 2020. "LTRWES: A new framework for security bug report detection." Information and Software Technology 124, no. : 106314.
In order to reduce overestimations of worst-case execution time (WCET), in this article, we firstly report a kind of specific WCET overestimation caused by non-orthogonal nested loops. Then, we propose a novel correction approach which has three basic steps. The first step is to locate the worst-case execution path (WCEP) in the control flow graph and then map it onto source code. The second step is to identify non-orthogonal nested loops from the WCEP by means of an abstract syntax tree. The last step is to recursively calculate the WCET errors caused by the loose loop bound constraints, and then subtract the total errors from the overestimations. The novelty lies in the fact that the WCET correction is only conducted on the non-branching part of WCEP, thus avoiding potential safety risks caused by possible WCEP switches. Experimental results show that our approach reduces the specific WCET overestimation by an average of more than 82%, and 100% of corrected WCET is no less than the actual WCET. Thus, our approach is not only effective but also safe. It will help developers to design energy-efficient and safe real-time systems.
Fanqi Meng; Xiaohong Su. Reducing WCET Overestimations by Correcting Errors in Loop Bound Constraints. Energies 2017, 10, 2113 .
AMA StyleFanqi Meng, Xiaohong Su. Reducing WCET Overestimations by Correcting Errors in Loop Bound Constraints. Energies. 2017; 10 (12):2113.
Chicago/Turabian StyleFanqi Meng; Xiaohong Su. 2017. "Reducing WCET Overestimations by Correcting Errors in Loop Bound Constraints." Energies 10, no. 12: 2113.
For safety-critical real-time software, if worst-case execution time (WCET) violates a time constraint, it is considered having a timeliness defect. To fix the defect early with lower cost, a WCET optimization strategy is proposed based on source code refactoring. The strategy guides programmers to search refactoring opportunities in the correct positions and perform refactorings by a reasonable sequence. To this end, the worst-case execution path (WCEP) of a target program is firstly extracted from its control flow graph. Then the WCEP is mapped onto source code by the back-annotation technique. An abstract syntax tree-based invariant path identification algorithm is developed for recognizing the invariant paths from the source-level WCEP. According to the invariant paths and loop statements, the source code is divided into four optimization regions with different priorities. Thus the searching scopes are reduced, and invalid refactorings are avoided. On the basis, the refactoring which has the lowest cost in the same region is performed first. To support the strategy, a cost model of source code refactoring is designed. It mainly considers adverse effects of refactorings on the maintainability of source code. The experimental results showed that the optimization strategy reduced WCET effectively and maximally kept the maintainability. Therefore it is more suitable for WCET optimization in an early programming phase. It is helpful to fix the defects early and then guarantee the timeliness safety of the software.
Fanqi Meng; Xiaohong Su. WCET optimization strategy based on source code refactoring. Cluster Computing 2017, 22, 5563 -5572.
AMA StyleFanqi Meng, Xiaohong Su. WCET optimization strategy based on source code refactoring. Cluster Computing. 2017; 22 (S3):5563-5572.
Chicago/Turabian StyleFanqi Meng; Xiaohong Su. 2017. "WCET optimization strategy based on source code refactoring." Cluster Computing 22, no. S3: 5563-5572.
When the multi-UAVs cooperatively attack multi-tasks, the dynamic changes of environments can lead to a failure of the tasks. So a novel path re-planning algorithm of multiple Q-learning based on cooperative fuzzy C means clustering is proposed. Our approach first reflects the dynamic changes of re-planning space by updating the fuzzy cooperative matrix. Then, the key way-points on the current global paths are used as the initial clustering centers for the cooperative fuzzy C means clustering, which generates the classifications of space points for multi-tasks. Furthermore, we use the classifications as the state space of each task and the fuzzy cooperative matrix as the reward function of the Q-learning. So a multi Q-learning algorithm is presented to synchronously re-plan the paths for multi-UAVs at every step. The simulation results show that the method subtracts the re-planning space of the tasks and improves the search efficiency of the learning algorithm.
Xiao-Hong Su; Ming Zhao; Ling-Ling Zhao; Yan-Hang Zhang. A Novel Multi Stage Cooperative Path Re-planning Method for Multi UAV. Computer Vision 2016, 482 -495.
AMA StyleXiao-Hong Su, Ming Zhao, Ling-Ling Zhao, Yan-Hang Zhang. A Novel Multi Stage Cooperative Path Re-planning Method for Multi UAV. Computer Vision. 2016; ():482-495.
Chicago/Turabian StyleXiao-Hong Su; Ming Zhao; Ling-Ling Zhao; Yan-Hang Zhang. 2016. "A Novel Multi Stage Cooperative Path Re-planning Method for Multi UAV." Computer Vision , no. : 482-495.
For the safety of real-time systems, it is very important that the execution time of programs must meet all time constraints, even under the worst case. To expose timeliness defects which may cause an execution timeout as early as possible, we have studied a novel nonlinear approach for estimating worst case execution time (WCET) during programming phase, called NL-WCET. In this paper, we propose a program features model, based on which NL-WCET employs least square support vector machine (LSSVM) to learn the program features, and then estimates WCET. To improve the accuracy of NL-WCET, we develop an algorithm for training samples optimization. The experimental results show that both the model and the algorithm have distinct effects on the accuracy of NL-WCET. When static similarity is \(\ge \)80 %, cosine similarity is \(\ge \)99.5 % and max quotient between corresponding items is \(\le \)50, the average error of NL-WCET is merely 0.82 %, quite lower than conventional WCET measurement. Meanwhile it also has higher efficiency than conventional WCET analysis. Thus NL-WCET is suitable for being used during programming phase, and can help programmers to discover timeliness defects as early as possible.
Fanqi Meng; Xiaohong Su; Zhaoyang Qu. Nonlinear approach for estimating WCET during programming phase. Cluster Computing 2016, 19, 1449 -1459.
AMA StyleFanqi Meng, Xiaohong Su, Zhaoyang Qu. Nonlinear approach for estimating WCET during programming phase. Cluster Computing. 2016; 19 (3):1449-1459.
Chicago/Turabian StyleFanqi Meng; Xiaohong Su; Zhaoyang Qu. 2016. "Nonlinear approach for estimating WCET during programming phase." Cluster Computing 19, no. 3: 1449-1459.
The cooperative multi-targets assignment for multiple unmanned aerial vehicles (UAV) is a complex combinatorial optimization problem. Multi-UAVs cooperation increases the scale of problems which cause a noticeable increase in task planning time. Moreover, it is difficult to build a unified assignment model because different tasks often require different numbers of UAVs and targets. Besides, the cooperative constraints of multi-UAVs in a three-dimensional environments are more complex than that in a two-dimensional environments, which makes it difficult to obtain an optimal solution. To solve these problems, we present a unified gene coding strategy to handle various models in a consistent framework. Then, a cooperative target assignment algorithm in a three-dimensional environments based on discrete mapping differential evolution is given. First, we use flight path cost to indicate the assignment relationship between the UAV and the target, which turns the optimization problem from discrete space to continuous space, and so the solving process can be simplified. Secondly, in order to obtain reasonable offspring for differential evolution, we map the solution back to the assignment relationship space according to inverse mapping rules. Finally, to avoid falling into a local optimal, a balance between exploration and exploitation is achieved by combining the dynamic crossover rate with the hybrid evolution strategy. The simulation results show that the proposed discrete mapping differential evolution algorithm with the unified gene coding strategy not only effectively solves the cooperative multi-targets assignment problem, but also improves the accuracy of the multi-targets assignment. It is also suitable for solving the large scale problem of assignment.
Zhao Ming; Zhao Lingling; Su Xiaohong; Ma Peijun; Zhang Yanhang. Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model. International Journal of Machine Learning and Cybernetics 2015, 8, 765 -780.
AMA StyleZhao Ming, Zhao Lingling, Su Xiaohong, Ma Peijun, Zhang Yanhang. Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model. International Journal of Machine Learning and Cybernetics. 2015; 8 (3):765-780.
Chicago/Turabian StyleZhao Ming; Zhao Lingling; Su Xiaohong; Ma Peijun; Zhang Yanhang. 2015. "Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model." International Journal of Machine Learning and Cybernetics 8, no. 3: 765-780.
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries, it is found that the energy efficiency and battery working temperature are closely related to the capacity degradation, which account for all performance metrics of lithium-ion batteries with regard to the RUL and the relationships between some performance metrics. Thus, we devise a non-iterative prediction model based on flexible support vector regression (F-SVR) and an iterative multi-step prediction model based on support vector regression (SVR) using the energy efficiency and battery working temperature as input physical characteristics. The experimental results show that the proposed prognostic models have high prediction accuracy by using fewer dimensions for the input data than the traditional empirical models.
Shuai Wang; Lingling Zhao; Xiaohong Su; Peijun Ma. Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression. Energies 2014, 7, 6492 -6508.
AMA StyleShuai Wang, Lingling Zhao, Xiaohong Su, Peijun Ma. Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression. Energies. 2014; 7 (10):6492-6508.
Chicago/Turabian StyleShuai Wang; Lingling Zhao; Xiaohong Su; Peijun Ma. 2014. "Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression." Energies 7, no. 10: 6492-6508.
Software with code clones is difficult for maintenance. It increases the cost of software maintenance. To solve the key problems of function optimization and parameter matching during the process of functionally equivalent code clone refactoring, this paper puts forward an approach for restructuring the fourth type (functionally equivalent) code clone by combining static analysis and dynamic testing. First, two kinds of function optimization strategy are proposed, i.e., running time and static characteristics. Then, determine the optimization function in each functionally equivalent code clone group according to the proposed optimization strategy. Finally, use the method of static analysis and dynamic testing to match the parameter matching for the replacement of procedure. On the basis of parameter matching, replace other clones with the optimization function and then complete the C code clone refactoring. Functionally equivalent C code clone refactoring system prototype is developed. Experimental results on the open source program show that the method can be accurately and effectively refactor the functionally equivalent C clone code.
Xiaohong Su; Fanlong Zhang; Xia Li; Peijun Ma; Tiantian Wang. Functionally Equivalent C Code Clone Refactoring by Combining Static Analysis with Dynamic Testing. Advances in Intelligent Systems and Computing 2013, 247 -256.
AMA StyleXiaohong Su, Fanlong Zhang, Xia Li, Peijun Ma, Tiantian Wang. Functionally Equivalent C Code Clone Refactoring by Combining Static Analysis with Dynamic Testing. Advances in Intelligent Systems and Computing. 2013; ():247-256.
Chicago/Turabian StyleXiaohong Su; Fanlong Zhang; Xia Li; Peijun Ma; Tiantian Wang. 2013. "Functionally Equivalent C Code Clone Refactoring by Combining Static Analysis with Dynamic Testing." Advances in Intelligent Systems and Computing , no. : 247-256.
In this paper, we investigate the multi-attribute group decision making (MAGDM) problem with incomplete attribute weight information under interval-valued intuitionistic fuzzy (IVIF) environment. Firstly, we propose a new axiomatic definition of the entropy on interval-valued intuitionistic fuzzy sets (IVIFSs) and a method to construct a set of entropies on IVIFSs. Secondly, we construct a decision making model based on aggregation operator and distance measures to solve the MAGDM problem under IVIF environment. In this model, we propose a method based on linear programming methodology using an accuracy function to choose the attribute weights for the decision making problem with partially known attribute weight information, and a method based on IVIF entropy to choose the attribute weights for the decision making problem with unknown attribute weight information. Finally, two numerical examples are given to demonstrate the feasibility and validity of the newly proposed MAGDM method by comparing it with other fuzzy MAGDM methods.
Ying-Jun Zhang; Pei-Jun Ma; Xiao-Hong Su; Chi-Ping Zhang. Multi-attribute Group Decision Making under Interval-valued Intuitionistic Fuzzy Environment. Acta Automatica Sinica 2012, 38, 220 -227.
AMA StyleYing-Jun Zhang, Pei-Jun Ma, Xiao-Hong Su, Chi-Ping Zhang. Multi-attribute Group Decision Making under Interval-valued Intuitionistic Fuzzy Environment. Acta Automatica Sinica. 2012; 38 (2):220-227.
Chicago/Turabian StyleYing-Jun Zhang; Pei-Jun Ma; Xiao-Hong Su; Chi-Ping Zhang. 2012. "Multi-attribute Group Decision Making under Interval-valued Intuitionistic Fuzzy Environment." Acta Automatica Sinica 38, no. 2: 220-227.