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Automatic fault localization is essential to intelligent software system. Most fault localization techniques assume the test oracle is perfect before debugging, which is hard to exist in practice. In fact, the test suite would contain a number of unlabelled test cases which have been proved to be useful in fault localization. However, due to the execution diversity, not all unlabelled test cases are suitable for fault localization. Selecting inappropriate unlabelled test cases can even weaken the fault localization efficiency. To solve the problem of filtering unlabelled test cases, this work aims to construct a feasible framework to select suitable unlabelled test cases for better fault localization. To address this issue, an entropy-based framework Efilter is constructed to filter unlabelled test cases. In Efilter, a Statement-based entropy and Testsuite-based entropy are constructed to measure the localization uncertainty of given test suite. The unlabelled test case with less Statement-based entropy or Testsuite-based entropy compared with its threshold would be selected. Further, the feature integration strategies for both Statement-based entropy and Testsuite-based entropy are given to calculate the suspiciousness of statements. The Efilter efficiency is evaluated across 6 open-source programs and 3 spectrum-based fault localizations. The results reveal that Efilter can improve fault localization efficiency by 18.8% and 16.5% with the Statement-based entropy and the Testsuite-based entropy respectively compared with the strategy without Efilter from the perspective of EXAM score on average. Our results indicate that the Efilter with both the Statement-based entropy and the Testsuite-based entropy can improve the fault localization in the scenario lack of test oracles, serving as an enhancement for fault localization in practice.
Yan Xiaobo; Liu Bin; Wang Shihai; An Dong; Zhu Feng; Yang Yelin. Efilter: An effective fault localization based on information entropy with unlabelled test cases. Information and Software Technology 2021, 134, 106543 .
AMA StyleYan Xiaobo, Liu Bin, Wang Shihai, An Dong, Zhu Feng, Yang Yelin. Efilter: An effective fault localization based on information entropy with unlabelled test cases. Information and Software Technology. 2021; 134 ():106543.
Chicago/Turabian StyleYan Xiaobo; Liu Bin; Wang Shihai; An Dong; Zhu Feng; Yang Yelin. 2021. "Efilter: An effective fault localization based on information entropy with unlabelled test cases." Information and Software Technology 134, no. : 106543.
Imbalanced data are a major factor for degrading the performance of software defect models. Software defect dataset is imbalanced in nature, i.e., the number of non-defect-prone modules is far more than that of defect-prone ones, which results in the bias of classifiers on the majority class samples. In this paper, we propose a novel credibility-based imbalance boosting (CIB) method in order to address the class-imbalance problem in software defect proneness prediction. The method measures the credibility of synthetic samples based on their distribution by introducing a credit factor to every synthetic sample, and proposes a weight updating scheme to make the base classifiers focus on synthetic samples with high credibility and real samples. Experiments are performed on 11 NASA datasets and nine PROMISE datasets by comparing CIB with MAHAKIL, AdaC2, AdaBoost, SMOTE, RUS, No sampling method in terms of four performance measures, i.e., area under the curve (AUC), F1, AGF, and Matthews correlation coefficient (MCC). Wilcoxon sign-ranked test and Cliff’s δ are separately used to perform statistical test and calculate effect size. The experimental results show that CIB is a more promising alternative for addressing the class-imbalance problem in software defect-prone prediction as compared with previous methods.
Haonan Tong; Shihai Wang; Guangling Li. Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction. Applied Sciences 2020, 10, 8059 .
AMA StyleHaonan Tong, Shihai Wang, Guangling Li. Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction. Applied Sciences. 2020; 10 (22):8059.
Chicago/Turabian StyleHaonan Tong; Shihai Wang; Guangling Li. 2020. "Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction." Applied Sciences 10, no. 22: 8059.
Automatic fault localization is essential for software engineering. However, fault localization suffers from the interactions among multiple faults. Our previous research revealed that the fault-coupling effect is responsible for the weakened fault localization performance in multiple-fault programs. On the basis of this finding, we propose a Test Case Restoration Method based on the Genetic Algorithm (TRGA) to search potential coupling test cases and conduct a restoration process for eliminating the coupling effect. The major contributions of the current study are as follows: (1) the construction of a fitness function to measure the possibility of failed test cases becoming coupling test cases; (2) the development of a TRGA that searches potential coupling test cases; (3) and an evaluation of the TRGA efficiency across 14 open-source programs, three spectrum-based fault localizations, and two parallel debugging techniques. The results revealed the TRGA outperformed the original fault localization techniques in 74.28% and 78.57% of the scenarios in the best and worst cases, respectively. On average, the percentage improvement was 4.43% for the best case and 2% for the worst case. A detailed discussion of TRGA parameter settings is also provided.
Yan Xiaobo; Liu Bin; Wang Shihai. A Test Restoration Method based on Genetic Algorithm for effective fault localization in multiple-fault programs. Journal of Systems and Software 2020, 172, 110861 .
AMA StyleYan Xiaobo, Liu Bin, Wang Shihai. A Test Restoration Method based on Genetic Algorithm for effective fault localization in multiple-fault programs. Journal of Systems and Software. 2020; 172 ():110861.
Chicago/Turabian StyleYan Xiaobo; Liu Bin; Wang Shihai. 2020. "A Test Restoration Method based on Genetic Algorithm for effective fault localization in multiple-fault programs." Journal of Systems and Software 172, no. : 110861.
It is popular to use software defect prediction (SDP) techniques to predict bugs in software in the past 20 years. Before conducting software testing (ST), the result of SDP assists on resource allocation for ST. However, DP usually works on fine-level tasks (or white-box testing) instead of coarse-level tasks (or black-box testing). Before ST or without historical execution information, it is difficult to get resource allocated properly. Therefore, a SDP-based approach, named DPAHM, is proposed to assist on arranging resource for coarse-level tasks. The method combines analytic hierarchy process (AHP) and variant incidence matrix. Besides, we apply the proposed DPAHM into a proprietary software, named MC. Besides, we conduct an up-to-down structure, including three layers for MC. Additionally, the performance measure of each layer is calculated based on the SDP result. Therefore, the resource allocation strategy for coarse-level tasks is gained according to the prediction result. The experiment indicates our proposed method is effective for resource allocation of coarse-level tasks before executing ST.
Can Cui; Bin Liu; Peng Xiao; Shihai Wang. Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing? Applied Sciences 2020, 10, 5372 .
AMA StyleCan Cui, Bin Liu, Peng Xiao, Shihai Wang. Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing? Applied Sciences. 2020; 10 (15):5372.
Chicago/Turabian StyleCan Cui; Bin Liu; Peng Xiao; Shihai Wang. 2020. "Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?" Applied Sciences 10, no. 15: 5372.
With the development of integrated modular avionics (IMA), the dynamic reconfiguration of IMA not only provides great advantages in resource utilization and aircraft configuration, but also acts as a valid means for resource failure management. It is vital to ensure the correction of the IMA dynamic reconfiguration process. The analysis of the dynamic reconfiguration process is a significant task. The Architecture Analysis & Design Language (AADL) is widely used in complicated real-time embedded systems. The language can describe the system configuration and the execution behaviors, such as configuration changes. Petri net is a widely used tool to conduct simulation analysis in many aspects. In this study, a model-based analyzing method with multiple constraints for the IMA dynamic reconfiguration process was proposed. First, several design constraints on the process were investigated. Second, the dynamic reconfiguration process was modeled based on the AADL. Then, a set of rules for the transition of the model from AADL to Petri net was generated, and the multi-constraints proposed were incorporated into Petri net for analysis. Finally, a simulation multi-constraint analysis with Petri net for the process of IMA dynamic reconfiguration was conducted. Finally, a case study was employed to demonstrate this method. This method is advantageous to the validity of IMA dynamic reconfiguration at the beginning of the system design.
Zeyong Jiang; Tingdi Zhao; Shihai Wang; Hongyan Ju. New Model-Based Analysis Method with Multiple Constraints for Integrated Modular Avionics Dynamic Reconfiguration Process. Processes 2020, 8, 574 .
AMA StyleZeyong Jiang, Tingdi Zhao, Shihai Wang, Hongyan Ju. New Model-Based Analysis Method with Multiple Constraints for Integrated Modular Avionics Dynamic Reconfiguration Process. Processes. 2020; 8 (5):574.
Chicago/Turabian StyleZeyong Jiang; Tingdi Zhao; Shihai Wang; Hongyan Ju. 2020. "New Model-Based Analysis Method with Multiple Constraints for Integrated Modular Avionics Dynamic Reconfiguration Process." Processes 8, no. 5: 574.
With the rapid development of high integrations in large complex systems, such as aircraft, satellite, and railway systems, due to the increasingly complex coupling relationship between components within the system, local disturbances or faults may cause global effects on the system by fault propagation. Therefore, there are new challenges in safety analysis and risk assessment for complex systems. Aiming at analyzing and evaluating the inherent risks of the complex system with coupling correlation characteristics objectively, this paper proposes a novel risk assessment and analysis method for correlation in complex system based on multi-dimensional theory. Firstly, the formal description and coupling degree analysis method of the hierarchical structure of complex systems is established. Moreover, considering the three safety risk factors of fault propagation probability, potential severity, and fault propagation time, a multi-dimensional safety risk theory is proposed, in order to evaluate the risk of each element within the system effecting on the overall system. Furthermore, critical safety elements are identified based on Pareto rules, As Low As Reasonably Practicable (ALARP) principles, and safety risk entropy to support the preventive measures. Finally, an application of an avionics system is provided to demonstrate the effectiveness of the proposed method.
Zeyong Jiang; Tingdi Zhao; Shihai Wang; Fuchun Ren. A Novel Risk Assessment and Analysis Method for Correlation in a Complex System Based on Multi-Dimensional Theory. Applied Sciences 2020, 10, 3007 .
AMA StyleZeyong Jiang, Tingdi Zhao, Shihai Wang, Fuchun Ren. A Novel Risk Assessment and Analysis Method for Correlation in a Complex System Based on Multi-Dimensional Theory. Applied Sciences. 2020; 10 (9):3007.
Chicago/Turabian StyleZeyong Jiang; Tingdi Zhao; Shihai Wang; Fuchun Ren. 2020. "A Novel Risk Assessment and Analysis Method for Correlation in a Complex System Based on Multi-Dimensional Theory." Applied Sciences 10, no. 9: 3007.
Software defect prediction based on supervised learning plays a crucial role in guiding software testing for resource allocation. In particular, it is worth noticing that using associative classification with high accuracy and comprehensibility can predict defects. But owing to the imbalance data distribution inherent, it is easy to generate a large number of non-defective class association rules, but the defective class association rules are easily ignored. Furthermore, classical associative classification algorithms mainly measure the interestingness of rules by the occurrence frequency, such as support and confidence, without considering the importance of features, resulting in combinations of the insignificant frequent itemset. This promotes the generation of weighted associative classification. However, the feature weighting based on domain knowledge is subjective and unsuitable for a high dimensional dataset. Hence, we present a novel software defect prediction model based on correlation weighted class association rule mining (CWCAR). It leverages a multi-weighted supports-based framework rather than the traditional support-confidence approach to handle class imbalance and utilizes the correlation-based heuristic approach to assign feature weight. Besides, we also optimize the ranking, pruning and prediction stages based on weighted support. Results show that CWCAR is significantly superior to state-of-the-art classifiers in terms of Balance, MCC, and Gmean.
Yuanxun Shao; Bin Liu; Shihai Wang; Guoqi Li. Software defect prediction based on correlation weighted class association rule mining. Knowledge-Based Systems 2020, 196, 105742 .
AMA StyleYuanxun Shao, Bin Liu, Shihai Wang, Guoqi Li. Software defect prediction based on correlation weighted class association rule mining. Knowledge-Based Systems. 2020; 196 ():105742.
Chicago/Turabian StyleYuanxun Shao; Bin Liu; Shihai Wang; Guoqi Li. 2020. "Software defect prediction based on correlation weighted class association rule mining." Knowledge-Based Systems 196, no. : 105742.
Cross-project defect prediction (CPDP) aims to predict defects of projects lacking training data by using prediction models trained on historical defect data from other projects. However, since the distribution differences between datasets from different projects, it is still a challenge to build high-quality CPDP models. Unfortunately, class imbalanced nature of software defect datasets further increases the difficulty. In this paper, we propose a transferlearning oriented minority over-sampling technique (TOMO) based feature weighting transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPDP by considering both classimbalance and feature importance problems. Differing from traditional over-sampling techniques, TOMO not only can balance the data but reduce the distribution difference. And then FWTNB is used to further increase the similarity of two distributions. Experiments are performed on 11 public defect datasets. The experimental results show that (1) TOMO improves the average G-Measure by 23.7\%$\sim$41.8\%, and the average MCC by 54.2\%$\sim$77.8\%. (2) feature weighting (FW) strategy improves the average G-Measure by 11\%, and the average MCC by 29.2\%. (3) TOMOFWTNB improves the average G-Measure value by at least 27.8\%, and the average MCC value by at least 71.5\%, compared with existing state-of-theart CPDP approaches. It can be concluded that (1) TOMO is very effective for addressing class-imbalance problem in CPDP scenario; (2) our FW strategy is helpful for CPDP; (3) TOMOFWTNB outperforms previous state-of-the-art CPDP approaches.
Haonan Tong; Bin Liu; Shihai Wang; Qiuying Li. Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction. 2019, 1 .
AMA StyleHaonan Tong, Bin Liu, Shihai Wang, Qiuying Li. Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction. . 2019; ():1.
Chicago/Turabian StyleHaonan Tong; Bin Liu; Shihai Wang; Qiuying Li. 2019. "Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction." , no. : 1.
Yan Xiaobo; Bin Liu; Wang Shihai. An Analysis on the Negative Effect of Multiple-Faults for Spectrum-Based Fault Localization. IEEE Access 2018, 7, 2327 -2347.
AMA StyleYan Xiaobo, Bin Liu, Wang Shihai. An Analysis on the Negative Effect of Multiple-Faults for Spectrum-Based Fault Localization. IEEE Access. 2018; 7 ():2327-2347.
Chicago/Turabian StyleYan Xiaobo; Bin Liu; Wang Shihai. 2018. "An Analysis on the Negative Effect of Multiple-Faults for Spectrum-Based Fault Localization." IEEE Access 7, no. : 2327-2347.
To ensure the rational allocation of software testing resources and reduce costs, software defect prediction has drawn notable attention to many “white-box” and “black-box” classification algorithms. Although there have been lots of studies on using software product metrics to identify defect-prone modules, defect prediction algorithms are still worth exploring. For instance, it is not easy to directly implement the Apriori algorithm to classify defect-prone modules across a skewed dataset. Therefore, we propose a novel supervised approach for software defect prediction based on atomic class-association rule mining (ACAR). It holds the characteristics of only one feature of the antecedent and a unique class label of the consequent, which is a specific kind of association rules that explores the relationship between attributes and categories. It holds the characteristics of only one feature of the antecedent and a unique class label of the consequent, which is a specific kind of association rules that explores the relationship between attributes and categories. Such association patterns can provide meaningful knowledge that can be easily understood by software engineers. A new software defect prediction model infrastructure based on association rules is employed to improve the prediction of defect-prone modules, which is divided into data preprocessing, rule model building and performance evaluation. Moreover, ACAR can achieve a satisfactory classification performance compared with other seven benchmark learners (the extension of classification based on associations (CBA2), Support Vector Machine, Naive Bayesian, Decision Tree, OneR, K-nearest Neighbors and RIPPER) on NASA MDP and PROMISE datasets. In light of software defect associative prediction, a comparative experiment between ACAR and CBA2 is discussed in details. It is demonstrated that ACAR is better than CBA2 in terms of AUC, G-mean, Balance and understandability. In addition, the average AUC of ACAR is increased by 2.9% compared with CBA2, which can reach 81.1%.
Yuanxun Shao; Bin Liu; Shihai Wang; Guoqi Li. A novel software defect prediction based on atomic class-association rule mining. Expert Systems with Applications 2018, 114, 237 -254.
AMA StyleYuanxun Shao, Bin Liu, Shihai Wang, Guoqi Li. A novel software defect prediction based on atomic class-association rule mining. Expert Systems with Applications. 2018; 114 ():237-254.
Chicago/Turabian StyleYuanxun Shao; Bin Liu; Shihai Wang; Guoqi Li. 2018. "A novel software defect prediction based on atomic class-association rule mining." Expert Systems with Applications 114, no. : 237-254.
Test resource constraints is a common phenomenon in software testing. Using defect prediction to guide the resource allocation can significantly improve the efficiency and effectiveness of available test resources. However, traditional defect prediction (t-DP) is a static strategy, where the predictor cannot be dynamically adjusted during the software testing process (STP). This paper combines defect prediction with feedback control in STP and proposes a feedback-based defect prediction model, where the test results generated during STP is used as feedback information for on-line adjustment of predictor to optimize the prediction result. In addition, a novel approach called feedback-based integrated prediction (FIP) is proposed to improve the prediction accuracy, where a global predictor and a local predictor are employed to make an integrated prediction using the weight to adjust the effects of predictors at different test stages. A systematic experiment is conducted to investigate the performance of the FIP over 10 public data sets. Results show that FIP has better prediction efficiency and better robustness for external data than the t-DP, especially when the percentage of the test modules is 40%.
Peng Xiao; Bin Liu; Shihai Wang. Feedback-based integrated prediction: Defect prediction based on feedback from software testing process. Journal of Systems and Software 2018, 143, 159 -171.
AMA StylePeng Xiao, Bin Liu, Shihai Wang. Feedback-based integrated prediction: Defect prediction based on feedback from software testing process. Journal of Systems and Software. 2018; 143 ():159-171.
Chicago/Turabian StylePeng Xiao; Bin Liu; Shihai Wang. 2018. "Feedback-based integrated prediction: Defect prediction based on feedback from software testing process." Journal of Systems and Software 143, no. : 159-171.
Integrated modular avionics (IMA) systems present many advantages. However, the resource sharing mechanism also brings a series of system problems, including the frequency of fault propagation and the difficulties of system design verification. The traditional analysis approaches for system designers have limits to analyze dynamic faults which are caused by unreasonable designs. These dynamic faults come up with component fault states, component state correlation, and system dynamic behaviors. In this paper, a new model-based dynamic analysis method for state correlation with IMA fault recovery is proposed, which helps to check system states and verify system designs by means of analyzing the dynamic behaviors of systems in a new view of systems' correlated states. A colored generalized stochastic Petri net (CGSPN) provides advantages to system modeling and simulation, but there are some difficulties for modeling component state correlations and system dynamic behaviors in detail on the IMA system. We make an improvement on CGSPN for modeling IMA by adding an element and changing fairing rules. In addition, multiconstraint specified to solve the configuration satisfying problem for IMA is built into the model. Afterward, according to results of model simulation, system dynamic faults are analyzed and system designs are checked, which will help to guide the system designers to adjust system architecture at the early stage of system development. Finally, a case study is given for demonstrating how to apply this new method.
Rongbin Han; Shihai Wang; Bin Liu; Tingdi Zhao; Zhiao Ye. A Novel Model-Based Dynamic Analysis Method for State Correlation With IMA Fault Recovery. IEEE Access 2018, 6, 22094 -22107.
AMA StyleRongbin Han, Shihai Wang, Bin Liu, Tingdi Zhao, Zhiao Ye. A Novel Model-Based Dynamic Analysis Method for State Correlation With IMA Fault Recovery. IEEE Access. 2018; 6 ():22094-22107.
Chicago/Turabian StyleRongbin Han; Shihai Wang; Bin Liu; Tingdi Zhao; Zhiao Ye. 2018. "A Novel Model-Based Dynamic Analysis Method for State Correlation With IMA Fault Recovery." IEEE Access 6, no. : 22094-22107.
Adaptive testing (AT) is a software testing approach that uses a feedback mechanism to enhance test effectiveness. Its testing strategy can be adjusted online by using the testing data collected during the software testing process. However, it requires complex parameter estimation which results in excessive computational overhead that may hinder the applicability of AT. In this paper, we propose an approach called AT based on moment estimation (AT-ME) to address this problem. The proposed approach uses moment estimation to serve as the algorithm of parameter estimation, which reduces the complexity of AT-ME. In addition, a dynamic length for testing action is set to limit the number of decisions without influencing the test effectiveness. The proposed approach has been validated on the Siemens test suite, which includes seven real programs. The experiments show that AT-ME can reduce the computational overhead of AT without compromising overall testing efficiency. Results demonstrate that AT-ME is a feasible and effective AT strategy.
Peng Xiao; Yongfeng Yin; Bin Liu; Bo Jiang; Yashwant K. Malaiya. Adaptive Testing Based on Moment Estimation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2017, 50, 911 -922.
AMA StylePeng Xiao, Yongfeng Yin, Bin Liu, Bo Jiang, Yashwant K. Malaiya. Adaptive Testing Based on Moment Estimation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017; 50 (3):911-922.
Chicago/Turabian StylePeng Xiao; Yongfeng Yin; Bin Liu; Bo Jiang; Yashwant K. Malaiya. 2017. "Adaptive Testing Based on Moment Estimation." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 3: 911-922.
Usually the complexity metric of software focuses on the complexity of code level, function level or structure level separately. It lacks of measurement for the comprehensive complexity of software system. This paper proposes a complexity metric model of three-level cascade network that based on complex network theory. In this metric model, the complexity of code level, function level and structure level are measured and the cascaded relationship between the three levels are analyzed. At last, the three-level cascade network model is built and the comprehensive complexity of software system is measured though the three-level cascade network model. The experiment result shows that the comprehensive complexity of the software system is correlated positively to the number of software defects.
Li Hanyan; Wang Shihai; Liu Bin; Xiao Peng. Software complexity measurement based on complex network. 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) 2017, 262 -265.
AMA StyleLi Hanyan, Wang Shihai, Liu Bin, Xiao Peng. Software complexity measurement based on complex network. 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). 2017; ():262-265.
Chicago/Turabian StyleLi Hanyan; Wang Shihai; Liu Bin; Xiao Peng. 2017. "Software complexity measurement based on complex network." 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) , no. : 262-265.
Petri nets are graphical and mathematical tools that are applicable to many systems for modeling, simulation, and analysis. With the emergence of the concept of partitioning in time and space domains proposed in avionics application standard software interface (ARINC 653), it has become difficult to analyze time-space coupling hazards resulting from resource partitioning using classical or advanced Petri nets. In this paper, we propose a time-space coupling safety constraint and an improved timed colored Petri net with imposed time-space coupling safety constraints (TCCP-NET) to fill this requirement gap. Time-space coupling hazard analysis is conducted in three steps: specification modeling, simulation execution, and results analysis. A TCCP-NET is employed to model and analyze integrated modular avionics (IMA), a real-time, safety-critical system. The analysis results are used to verify whether there exist time-space coupling hazards at runtime. The method we propose demonstrates superior modeling of safety-critical real-time systems as it can specify resource allocations in both time and space domains. TCCP-NETs can effectively detect underlying time-space coupling hazards.
Zelin Li; Shihai Wang; Tingdi Zhao; Bin Liu. A hazard analysis via an improved timed colored petri net with time–space coupling safety constraint. Chinese Journal of Aeronautics 2016, 29, 1027 -1041.
AMA StyleZelin Li, Shihai Wang, Tingdi Zhao, Bin Liu. A hazard analysis via an improved timed colored petri net with time–space coupling safety constraint. Chinese Journal of Aeronautics. 2016; 29 (4):1027-1041.
Chicago/Turabian StyleZelin Li; Shihai Wang; Tingdi Zhao; Bin Liu. 2016. "A hazard analysis via an improved timed colored petri net with time–space coupling safety constraint." Chinese Journal of Aeronautics 29, no. 4: 1027-1041.
To optimise the configuration and reconfiguration strategies in integrated modular avionics (IMA), modelling and evaluating the reliability of the reconfigurable IMA system have drawn great attention from system engineers and designers. Currently most researches regard each configuration as a single system state, but the detailed system architecture under each configuration or the events that trigger the reconfiguration behaviour are not involved. In this study, a modelling method is proposed for these complex processes of the IMA reconfiguration. It employs architecture analysis and design language (AADL) along with its Error Model Annex, ARINC653 Annex, and mode transition mechanisms to model the correlated component error state transitions, system configuration architectures, and reconfiguration behaviours. Based on the proposed AADL modelling approach, these model information is extracted purposefully to establish computable models. Then a reliability analysis approach is proposed which is able to result in various reliability attributions, such as component reliability, configuration reliability under the software architecture, single task reliability, and multi-tasking reliability. These evaluation processes take into account both of the fault isolation reconfiguration and task mode reconfiguration for the first time. Finally, the case study is involved, which demonstrates the feasibility of the analysis approach for IMA based on an AADL model.
Quan Zhang; Shihai Wang; Bin Liu. Approach for integrated modular avionics reconfiguration modelling and reliability analysis based on AADL. IET Software 2016, 10, 18 -25.
AMA StyleQuan Zhang, Shihai Wang, Bin Liu. Approach for integrated modular avionics reconfiguration modelling and reliability analysis based on AADL. IET Software. 2016; 10 (1):18-25.
Chicago/Turabian StyleQuan Zhang; Shihai Wang; Bin Liu. 2016. "Approach for integrated modular avionics reconfiguration modelling and reliability analysis based on AADL." IET Software 10, no. 1: 18-25.
This paper aims to review and summarise the researches on the analysis and simulation of avionics including integrated modular avionics (IMA) based on Architecture Analysis & Design Language (AADL) models. Firstly, the features of AADL and its annexes are introduced in brief including the analysis and test tools. Then, a brief introduction on IMA is made by describing the characteristics which take new challenges to system analysing and testing. After that, a comparison between AADL and UML is laid out to weigh out both pros and cons for avionics analysis. Here, introductions and comparisons of current researches for avionics system analysis are made and the corresponding tools for test and simulation are concluded. At last, future works for avionics analysis and simulation are laid out.
Xiaoxu Diao; Bin Liu; Shihai Wang. A survey of avionics analysis and simulation based on AADL model. International Journal of Information and Communication Technology 2016, 9, 282 .
AMA StyleXiaoxu Diao, Bin Liu, Shihai Wang. A survey of avionics analysis and simulation based on AADL model. International Journal of Information and Communication Technology. 2016; 9 (3):282.
Chicago/Turabian StyleXiaoxu Diao; Bin Liu; Shihai Wang. 2016. "A survey of avionics analysis and simulation based on AADL model." International Journal of Information and Communication Technology 9, no. 3: 282.
Aiming at the problem of reconfiguration strategy of integrated avionics system, a method of static load balancing strategy optimization based on graph theory is proposed after satisfying system performance and reliability requirements. This method establishes the IMA static load balancing strategy analysis model based on graph theory and puts forward the load balance evaluation indexes of IMA system:The maximum load value of the system partition obtained by allocating the maximum load value of the processor according to the time slice length;the longest communication link for the transmission of information between tasks;and the maximum traffic volume on the communication line. With the three evaluation indexes synthesizing, the unitary evaluation index of the IMA system load balancing strategy is put forward. On the basis of this method, the future research direction of load balancing of IMA system is prospected.
Siyuan Zhou; Zhijuan Zhan; Shihai Wang. An IMA Static Load Balancing Strategy Optimization Method Based on Graph Theory. Journal of Electronics and Information Science 2016, 1, 32 -36.
AMA StyleSiyuan Zhou, Zhijuan Zhan, Shihai Wang. An IMA Static Load Balancing Strategy Optimization Method Based on Graph Theory. Journal of Electronics and Information Science. 2016; 1 (1):32-36.
Chicago/Turabian StyleSiyuan Zhou; Zhijuan Zhan; Shihai Wang. 2016. "An IMA Static Load Balancing Strategy Optimization Method Based on Graph Theory." Journal of Electronics and Information Science 1, no. 1: 32-36.
This paper aims to review and summarise the researches on the analysis and simulation of avionics including integrated modular avionics (IMA) based on Architecture Analysis & Design Language (AADL) models. Firstly, the features of AADL and its annexes are introduced in brief including the analysis and test tools. Then, a brief introduction on IMA is made by describing the characteristics which take new challenges to system analysing and testing. After that, a comparison between AADL and UML is laid out to weigh out both pros and cons for avionics analysis. Here, introductions and comparisons of current researches for avionics system analysis are made and the corresponding tools for test and simulation are concluded. At last, future works for avionics analysis and simulation are laid out.
Shihai Wang; Xiaoxu Diao; Bin Liu. A survey of avionics analysis and simulation based on AADL model. International Journal of Information and Communication Technology 2016, 9, 282 .
AMA StyleShihai Wang, Xiaoxu Diao, Bin Liu. A survey of avionics analysis and simulation based on AADL model. International Journal of Information and Communication Technology. 2016; 9 (3):282.
Chicago/Turabian StyleShihai Wang; Xiaoxu Diao; Bin Liu. 2016. "A survey of avionics analysis and simulation based on AADL model." International Journal of Information and Communication Technology 9, no. 3: 282.
The structure of the embedded system gets much more complicated. Current basic Architecture Analysis and Design Language (AADL) reliability model cannot meet the requirements of software reliability being evaluated while being designed. For the present, reliability evaluated needs abundant fault analysis which can not be realized in the early of software development. The article has come up with a methodology based on system architecture using AADL to perform reliability evaluate at Early development With good understanding of rules and transformation from AADL based system architecture model to Petri Net, a one-to-one mapping rule was achieved between AADL elements and Petri Net elements. Using current mathematic model of Petri Net to evaluate the reliability of software architecture. At last, a flight control system of AADL model was given as an example to validate the availability of the given method
Dongyi Ling; Shihai Wang; Bin Liu; Xiaoqi Xing. Reliability Evaluation based on the AADL Architecture Model. Journal of Networks 2014, 9, 2721-2727 .
AMA StyleDongyi Ling, Shihai Wang, Bin Liu, Xiaoqi Xing. Reliability Evaluation based on the AADL Architecture Model. Journal of Networks. 2014; 9 (10):2721-2727.
Chicago/Turabian StyleDongyi Ling; Shihai Wang; Bin Liu; Xiaoqi Xing. 2014. "Reliability Evaluation based on the AADL Architecture Model." Journal of Networks 9, no. 10: 2721-2727.