Rule-based static analysis tools are widely utilized for their high customizability. However, the creation of effective rules presents significant challenges, including the considerable human effort to handle the complexity of rules, and the additional costs involved in developing rules across various programming languages or frameworks. To address the significant challenges in manual rule creation for static analysis, this paper proposes a novel framework that leverages large language models (LLMs) to automate the generation of static analysis rules. The framework is specifically designed to alleviate the substantial human effort typically required in constructing and maintaining rule sets. Furthermore, we introduce a natural language-mediated rule migration methodology, which ensures semantic consistency when transferring functionally similar rules across different programming languages or frameworks. By seamlessly integrating LLMs with existing static analysis tools, our approach not only enhances the scalability and adaptability of rule generation but also enables efficient vulnerability scanning without the need for extensive computational resources such as large GPU clusters. This integration aims to bridge the gap between natural language understanding and program analysis, thereby facilitating more intelligent and resource-efficient static analysis. The method achieved 81.98 % grammatical and 74.73 % functional validity in rule generation for Semgrep, while migrating rules across 4 languages (Python, Java, JavaScript, and Golang). On the real-world engineering evaluation, it uncovered 9 unknown vulnerabilities in the latest version of the Linux Kernel undetectable by now. This work highlights the potential of our LLM-driven framework provides better handling of corner cases and maintains compatibility with industry-standard tools.

Large Language Models (LLMs) have become transformative tools in natural language processing, significantly advancing the fields of communication, information analysis, and knowledge sharing. However, the vast amounts of sensitive data they handle pose significant challenges for data security and privacy. Traditional cryptographic methods face limitations in efficiently managing access control in LLM-driven electrical distributed systems. This paper introduces a novel Privacy-preserving Revocable Access Control for LLM-driven electrical distributed systems, addressing key concerns such as access policy concealment, user revocation efficiency, and computational overhead. Leveraging an inner-product-based access control mechanism, the proposed scheme achieves complete access policy concealment while supporting flexible access control with wildcard capabilities. Additionally, it facilitates efficient user revocation without requiring costly updates to ciphertexts in such an LLM-driven electrical distributed system. The concise algorithmic structure ensures high efficiency, further enhanced through online/offline encryption and outsourced decryption mechanisms.

Cyber-Physical Systems (CPS) confront significant challenges in the assessment of state after experiencing disturbances or attacks, attributed to their inherent complexity. This situation demands comprehensive and expensive experiments for evaluation. Employing black-box optimization methods to optimize test data generation proves efficacious. Nevertheless, prevailing black-box optimization techniques often prioritize trade-offs among objectives, neglecting the search space’s multimodality. To bridge this divide, we draw inspiration from multi-objective multimodal optimization problems (MMOPs) to address black-box optimization problems, proposing a multimodal multi-objective test data generation method (MMOTDG) for testing the state of CPS under disturbances and attacks. The clustering-based particle swarm optimization leveraging adaptive resonance theory, termed CARTPSO, is employed to solve MMOPs in the test data generation process. Experiment results demonstrate that CARTPSO shows significantly superior performance to five leading multimodal multi-objective algorithms across 11 benchmark functions. A novelty co-simulation testing environment is built for testing the state of aircraft encountering wind disturbance in a black-box manner. The proposed MMO-TDG is applied in this environment to generate test data against random search and NSGAII-based test data generation method. Results show that test data generated by MMO-TDG not only exhibit diversity but also effectively fulfill the testing objectives.

Epigenetics’ flexibility in terms of finer manipulation of genes renders unprecedented levels of refined and diverse evolutionary mechanisms possible. From the epigenetic perspective, the main limitations to improving the stability and accuracy of genetic algorithms are as follows: (1) the unchangeable nature of the external environment, which leads to excessive disorders in the changed phenotype after mutation and crossover; (2) the premature convergence due to the limited types of epigenetic operators. In this paper, a probabilistic environmental gradient-driven genetic algorithm (PEGA) considering epigenetic traits is proposed. To enhance the local convergence efficiency and acquire stable local search, a probabilistic environmental gradient (PEG) descent strategy together with a multi-dimensional heterogeneous exponential environmental vector tendentiously generates more offsprings along the gradient in the solution space. Moreover, to balance exploration and exploitation at different evolutionary stages, a variable nucleosome reorganization (VNR) operator is realized by dynamically adjusting the number of genes involved in mutation and crossover. Based on the above-mentioned operators, three epigenetic operators are further introduced to weaken the possible premature problem by enriching genetic diversity. The experimental results on the open Congress on Evolutionary Computation-2017 (CEC’ 17) benchmark over 10-, 30-, 50-, and 100-dimensional tests indicate that the proposed method outperforms 10 state-of-the-art evolutionary and swarm algorithms in terms of accuracy and stability on comprehensive performance. The ablation analysis demonstrates that for accuracy and stability, the fusion strategy of PEG and VNR are effective on 96.55% of the test functions and can improve the indicators by up to four orders of magnitude. Furthermore, the performance of PEGA on the real-world spacecraft trajectory optimization problem is the best in terms of quality of the solution.

ABSTRACT Accurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

Accurate fault localization renders software test resource allocation and maintenance cost-efficient. However, this is challenging when there are false alarm repercussions caused by module coupling of complex software. In this article, therefore, we propose a new method for multiple software fault localization from the perspective of network spectrum based on a graph neural network model. First, we constructed the network model of the software under test to represent the coupling relationships among software modules based on complex network theory. In addition, test suits were executed and recorded to construct the program spectrum. Subsequently, the software network and program spectrum were fused into the network spectrum, and we reprocessed it with feature dimension reduction, normalization, and graph-based class-imbalance treatment. The graph neural network was then used to construct a multiple-fault location model based on the processed network spectrum. Empirical studies were performed on the Defects4J dataset. The experimental results indicated that the proposed method outperformed six baseline methods (with an average improvement of 13.03% on the T-EXAMscore). This study is expected to provide insights into more smart software quality and reliability assurance.

Effective software defect prediction (SDP) is important for software quality assurance. Numerous advanced SDP methods have been proposed recently. However, how to consider the task correlations and achieve multi-objective SDP accurately and efficiently still remains to be further explored. In this paper, we propose a novel multi-objective SDP method via multi-source uncertain information fusion and multi-task multi-view learning (MTMV) to accurately and efficiently predict the proneness, location, and type of defects. Firstly, multi-view features are extracted from multi-source static analysis results, reflecting uncertain defect location distribution and semantic information. Then, a novel MTMV model is proposed to fully fuse the uncertain defect information in multi-view features and realize effective multi-objective SDP. Specifically, the convolutional GRU encoders capture the consistency and complementarity of multi-source defect information to automatically filter the noise of false and missed alarms, and reduce location and type uncertainty of static analysis results. A global attention mechanism combined with the hard parameter sharing in MTMV fuse features according to their global importance of all tasks for balanced learning. Then, considering the latent task and feature correlations, multiple task-specific decoders jointly optimize all SDP tasks by sharing the learning experience. Through the extensive experiments on 14 datasets, the proposed method significantly improves the prediction performance over 12 baseline methods for all SDP objectives. The average improvements are 30.7%, 31.2%, and 32.4% for defect proneness, location, and type prediction, respectively. Therefore, the proposed multi-objective SDP method can provide more sufficient and precise insights for developers to significantly improve the efficiency of software analysis and testing.

Accurate transmission performance prediction of the balise system is important for reliable ground–train communication in high-speed rails. However, the combined effects of ground–train coupling and on-board demodulation make it difficult to predict long-term and volatile holistic transmission performance under multisource disturbances. To address these issues, this article proposes a gate-steered residual interweave architecture. A new gated double-dilated temporal convolutional network (GDTCN) is introduced by combining large and small receptive fields with gating units to extract multiscale features and filter out useless information. It can learn multisource parameter disturbances induced by transient coupling and intermittent demodulation to reduce the prediction errors of occasional volatilities in holistic transmission performance. A new residual interweave GDTCN structure with linear gated cross connections is built to remit the differences between signal and telegram parameter features in view of the inconsistent data transfer modes during coupling and demodulation. This structure can facilitate multipath feature interaction and fusion to enrich discriminative correlation information, thereby improving the prediction accuracy of holistic transmission performance. An adaptive gated attention mechanism is designed to exploit correlation and dependent information between coupling and demodulation in a weighted fusion manner. It can minimize prediction error accumulation to enhance long-term holistic transmission performance forecasting. Extensive experiments demonstrate that the proposed architecture can perform predictions with high accuracy and efficiency under different rail conditions. The proposed method can provide early warning before ground balise or on-board module failures to improve the reliability and availability of ground–train communication.

Effective testing methods have been proposed to verify the reliability and robustness of Deep Neural Networks (DNNs). However, enhancing their adversarial robustness against various attacks and perturbations through testing remains a key issue for their further applications. Therefore, we propose DeepRTest, a white-box testing framework for DNNs guided by vulnerability to effectively test and improve the adversarial robustness of DNNs. Specifically, the test input generation algorithm based on joint optimization fully induces the misclassification of DNNs. The generated high neuron coverage inputs near classification boundaries expose vulnerabilities to test adversarial robustness comprehensively. Then, retraining based on the generated inputs effectively optimize the classification boundaries and fix the vulnerabilities to improve the adversarial robustness against perturbations. The experimental results indicate that DeepRTest achieved higher neuron coverage and classification accuracy than baseline methods. Moreover, DeepRTest could improve the adversarial robustness by 39% on average, which was 12.56% higher than other methods.

ZigBee is one of the communication protocols used in the Internet of Things (IoT) applications. In typical deployment scenarios involving low-cost and low-power IoT devices, many communication features are disabled, consequently affecting the security offered by ZigBee. The ZigBee specification assumes that deployment of frame counters is sufficient to mitigate replay attacks in secure ZigBee networks. However, we demonstrate that it is insufficient in this paper (i.e., the network is no longer secure after the coordinator restarts). As a countermeasure, we present a timestamp-based scheme to mitigate replay attacks. Our mitigation strategy does not consume power significantly, and fully powered devices will be responsible for providing power-constrained devices with the current timestamp. The proposed scheme is designed for all ZigBee topologies and different states of ZigBee End Devices (ZEDs). Findings from our evaluation show that the proposed scheme can successfully mitigate replay attacks, with no significant network performance degradation even assuming a worst-case scenario (i.e., many devices are sending data simultaneously).

While a number of network systems are running under certain stress with a limited lifetime, it is still unknown how to predict the lifetime–stress relation of complex systems. We develop a percolation-based approach to build an operational lifetime–stress model for complex networks, which captures the spatial and temporal reliability characteristics of the system. In this article, the general analytical expression for the entire operational lifetime–stress relation has been presented, which suggests that the load stress and the number of nodes in the network impact the operational lifetime in the same manner. The size effect found here in the lifetime–stress relation is observed for the first time, to our best knowledge. For 2-D lattices, we show that the lifetime–stress relation can be regarded as the combination of two parts—an approximately linear region for small stress, and nonlinear region for large stress. We also analyze the lifetime–stress function of Beijing road network and the western United States power grid. Our article might help to develop acceleration testing methods, which will facilitate better design of reliable complex systems.

Software reliability plays an important role in modern society. To evaluate software reliability, software reliability growth models (SRGMs) investigate the number of software faults in the testing phase. Obviously, testing progresses are inevitably influenced by dynamic indeterministic fluctuations such as the testing effort expenditure, testing efficiency and skill, testing method, and strategy. To model these dynamic fluctuations, several probability theory-based SRGMs are proposed. However, probability theory is suitable for dealing with aleatory uncertainty, but fails to deal with epistemic uncertainty widely existing in software faults. Therefore, this article considers software reliability from a new perspective under the framework of uncertainty theory, which is a new mathematical system different from probability theory, and proposes a software belief reliability growth model (SBRGM) based on uncertain differential equations for the first time. Based on this SBRGM, properties of essential software reliability metrics are investigated under belief reliability theory, which is a brand-new reliability theory. Parameter estimations for unknown parameters in SBRGM are presented. Furthermore, some numerical examples and real data analyses illustrate our methodology in detail, and show that it performs better than several famous probability-based SRGMs in terms of fitting ability and prediction ability. Finally, an optimal software release policy is discussed.

Accurate software bug number prediction makes software test resource allocation, maintenance, and release time cost efficient. However, it is a challenge to accurately predict the number of software bugs when there fluctuations caused by many uncertain factors faced by the complex software. Considering this, a new method for software bug number prediction based on a panel data model from the perspective of complex networks is proposed in this article. Using complex network theory, we constructed the software code network and calculated the static metrics of the network structure, and the percolation threshold of change in the network structure based on percolation theory as a dynamic metric. These network metrics were then normalized as inputs and a panel data model was used for bug prediction. The proposed method can predict the number of bugs for both within-project and cross-project. Empirical studies were performed on data obtained from 120 releases of eight open-source software projects (Lua, SQLite, Redis, Linux kernel, ant, jmeter, poi, and tomcat), the experimental results indicated that network metrics are effective bug indicators, and the proposed method outperformed ten baseline methods (with an average improvement of 28.05%). This article is expected to provide insights into more smart software quality and reliability assurance.

The flight reliability has been receiving considerable attention. However, the ability of the aircraft recovers to normal flight state from a perturbation were not considered under most circumstances. In this study, a simulation based intelligent analysis framework is proposed to identify the reliability, resilience and vulnerability states of Boeing 737 MAX aircraft disturbed by Maneuvering Characteristics Augmentation System (MCAS) system abnormal activation during the flight. Multiswarm particle swarm optimization (multiswarm PSO) algorithm based test cases generation strategy, aircraft failure behavior model which reflects aerodynamics of the aircraft after the horizontal stabilizer deflection caused by MCAS abnormal activation, JSBSim and FlightGear based co-simulation with aerodynamic and visual characteristics and neural network based flight states identification method constitute the proposed framework. Study results show that the proposed method can cover the margin of resilience and vulnerability quickly and the classification model can identify aircraft flight reliability, resilience and vulnerability states corresponding to different inputs accurately. The proposed framework can be used to validate the flight reliability and system resilience in a more efficient way.

In this paper, we present a study on person identification using static features extracted from Kinect skeleton data. On the contrary to previous reports that the dynamic features such as gait parameters are more discriminative than static features, we find that by using a combination of a set of easy to obtain static features, we can achieve nearly perfect accuracy in identifying persons with only a few frames. In our study, we experimented with several classifiers, including k-nearest neighbor (KNN), decision tree, Gaussian Naive Bayesian, neural network with multiplayer perception (MLP), and support vector machine (SVM), and several combinations of static skeleton features. In all scenarios, KNN outperforms other classifiers consistently. MLP and SVM require a huge amount of parameter tuning and training time and they do not perform well compared with KNN except for small gallery sizes when all static features available.

Abstract In the current era of technology and development, component‐based software development (CBSD) has been progressively implemented. Components are used in large products for multiple users with diverse viewpoints and are highly configurable to provide higher satisfaction. Components are reused because of their similar functionality to reduce complexity and ensure the correct interaction between interfaces during product development. However, implementation of component functionalities creates complications due to improper specification, prioritization of components requirements, encapsulated functionalities, and more human interaction. Furthermore, due to configurability and the involvement of multiple stakeholders, ambiguity and semantic issues arise in the behaviour of reusable components. To overcome semantic‐based specification and prioritization components' related issues, we propose a framework that uses text mining and case‐based reasoning (CBR) techniques. Results of our empirical evaluation demonstrate that the proposed framework significantly outperforms the conventional technique.

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.

In this article, we provide a concise but systematic review on blockchain-enabled cyber-physical systems (CPS). We dissect various blockchain-enabled CPS as reported in the literature in terms of their operations and the features of blockchain that have been used. We identify key common CPS operations that can be enabled by blockchain, and classify them in terms of their time sensitivity and throughput requirements. We also elaborate and classify features of blockchain in terms of different levels of benefits to CPS, including security, privacy, immutability, fault tolerance, interoperability, data provenance, atomicity, automation, data/service sharing, and trust. Finally, we point out two primary open research issues for developing blockchain-enabled CPS, namely, excessive delay in reaching consensus and limited throughput, and outline future research directions.

Although several studies have been devoted to the reliability analysis of phased-mission systems (PMSs) considering the influence of common cause failures (CCFs), statistical correlation and repairable behavior still pose challenges to the analysis. In this article, a hierarchical formal model is proposed for the reliability analysis of repairable PMSs with CCFs. The low-level model is based on continuous time Markov chains and multiple beta factor theory used to construct the failure and repair behaviors of different missions under the effect of CCFs. The upper-level model realizes phase transition based on the Erlang distribution. The model can be implemented in PRISM, a tool that supports probabilistic model checking technology, and can be automatically verified by the properties (termed as reliability and availability in this article) defined by continuous stochastic logic. The proposed model introduces the benefits of probabilistic model checking into the analysis of PMSs for the first time. The formal hierarchical model is demonstrated by an example of a field programmable gate array system based on different design modes. Then, the influence of CCFs on the reliability of three different mission phase combinations are discussed. Our model can help researchers verify system properties for dynamic, complex, and continuous tasks in the initial design phase, thereby optimizing the design pattern or task arrangement.

As an important component of space electronic systems, static random-access memory based field programmable gate arrays (FPGAs) are inevitably affected by single-event upsets, including single-cell upset (SCU) and multiple-cell upset (MCU), caused by space radiation. In particular, as the technology scales down, the probability of MCU increases significantly; however, the analysis of MCU remains an open challenge. Based on the common cause failure theory and continuous-time Markov chain, this article proposes a new hybrid model to quantify the coexistence effect of MCU and SCU on mitigation design modes with different combinations of strategies, such as triple modular redundancy, partition, and scrubbing. Synchronization and interleaving modeling technologies are used to construct the concurrent behavior of an FPGA system under different mitigation strategies and MCU problems. In addition, we introduce a partition factor to quantitatively explain the phenomena and laws of MCUs acting on adjacent partitions. The proposed method is demonstrated using TMR with scrubbing and partition strategies, which verify that the existence of MCU reduces the system reliability and availability, and the scrubbing and partition strategy are effective against MCU issues. More importantly, the limitations of mitigation strategies in different occurrence probabilities of MCU can be found by the proposed method. In summary, the analysis and discussion presented in this article can provide useful insights for relevant designers to select and optimize different design patterns of a system operating in a dynamic and complex radiation environment.

As an important part of space electronic system, static random-access memory (SRAM)-based field-programmable gate arrays (FPGAs) are inevitably affected by single-event upsets caused by space radiation. Although triple-modular redundancy, as one of the main mitigation strategies, plays an important role in improving the system reliability, the common cause failure (CCF) in redundant components is still one of the factors threatening the system reliability. In addition, CCF increases the complexity of reliability analysis when considering the implementation of phased mission. We propose an effective method to incorporate CCF into the reliability analysis of the phased-mission system (PMS). Based on the continuous-time Markov chain and multiple beta factor theory, we establish the dynamic behavior model of the system considering CCF under single-phase condition, and realize the transformation of multi-phase tasks based on the Erlang distribution. Our method can be easily implemented in PRISM, a probabilistic model checker, in which various properties of the system can be automatically verified. The analysis and discussion of this paper can provide useful insights for relevant researchers and realize automatic reliability analysis.

In this paper, we present an exploratory work towards the recognition of activities and performing real-time objective assessment in human patient simulation (HPS). Although HPS has been pervasively used in medical and nursing programs in developed countries, there is a huge need in providing consistent and objective assessment on student performance during HPS. Current methods all depend on instructor subjective observation, which not only could lead to inconsistency in evaluation across different students and different instructors, but also are very time and resource intensive. Recognizing complex human activities in the context of HPS is very challenging because it involves the recognition of human actions, gestures, as well as human-object and human-mannequin interactions. Hence, we study the feasibility of developing such a system for a particular simulation where a student is required to first identify the patient and then place a neck brace on the patient's neck. The system we that we have developed identifies the actions and activities in the simulation and provides qualitative assessment on the student performance using computer vision, OpenPose, and TensorFlow. The system also consists of a debriefing mobile app that the student and instructor could use to view an automatically generated report with supporting key frames captured and annotated by our system.

Operating systems are critical infrastructures for the information systems. Malfunction of certain function component can induce unexpected risks and countless damage for the computing service based on the operating systems. While it is critical for understanding the failure mechanism of operating system, it remains unclear how the function components interact with each other. Here we study these interactions in the kernel of Android OS by modeling the operating system as a complex network. In this network, each node represents a function and links are various call relationship between them. With community analysis, we find three different relations between the topological statistics and the community size. To reveal the organization vulnerability in different scale, we also perform the percolation analysis and identify the critical structures of this software networks. Our findings may help to understand the system complexity and design corresponding software testing methods.

With the advent of smart, inexpensive devices and a highly connected world, a need for smart service discovery, delivery, and adaptation has appeared. This interconnection is composed of sensors within devices or placed externally in the surrounding environment. Our research addresses this need through a context aware system, which adapts to the users’ context. Given that the devices are mobile and battery operated, the main challenge in a context awareness approach is power conservation. The devices are composed of small sensors that consume power in the order of a few mW. However, their consumptions increase manifold during data processing. There is a need to conserve power while delivering the requisite functionality of the context aware system. Therefore, this feature is termed as ‘power awareness.’ In this paper, we describe different power awareness techniques and compare them in terms of their conservation effectiveness. In addition, based on the investigations and comparison of the results, a power aware framework is proposed for a context aware system.

Monitoring and predicting the increasing or decreasing trend of bug number in a software is of great importance to both software developers and users. Accurate predicting of number of software bug will help developers make timely and correct decision. For software users, knowing the possible number of software bugs will enable them to take seasonable actions to cope with loss caused by possible software bugs. Medical software is vital to people's health, and therefore, the bug prediction of medical software is more important and essential than the ordinary software. To accomplish this goal, we present a method based on static analysis and correlation analysis to predict the bug number of medical image informatics software 3D Slicer and ITK. We obtain the complexity metrics through static analysis, then get the bug predicted value via correlation analysis between existing bug number and complexity metrics. The core idea of this paper is that the changes of software complexity metrics obtained by static analysis can reflect the changes of the bug number, and the predicted results prove the feasibility of this method. In addition, the method proposed in this paper is also applicable to other types of software other than medical software.

Context Genetic algorithm (GA) is an important intelligent method in the area of automatic software test data generation. However, existing GAs tend to get trapped in the local optimal solution, leading to population aging, which can significantly reduce the benefits of GA-based software testing and increase cost and effort. Although much attention has been focused on solving this problem by improving chromosome population, genetic operations, and genetic parameters adjustment, the applicability of most of the algorithms proposed is often narrow because of the complex operations involved and nondeterminism inherited from traditional GAs. Objectives This paper proposes a new algorithm called the regenerate genetic algorithm (RGA), which is based on a new simple, stable, and easy-to-implement regeneration strategy that involves judging the population aging process. Methods We propose a new regeneration strategy—called Regenerate Genetic Algorithm (RGA)—that solves these problems easily and effectively. The proposed strategy defines population aging factors and process in order to determine the degree of population aging. Subsequently, when population aging has reached a certain limit, a population regeneration operation is triggered. In contrast to other improved methods, the proposed regeneration strategy for population aging easily achieves a stronger ability to jump out of the local optimal solution, thereby preventing population aging and effectively improving test coverage, without modifying any parameter of the original GA. Results The proposed algorithm is experimentally evaluated by comparing it to the basic GA, Random Testing (RT) and several other methods in terms of both efficiency and effectiveness on the Siemens Suite of test programs and a more complex real program. The results obtained indicate that the proposed algorithm can effectively increase search efficiency, restrain population aging, increase test coverage, and reduce the number of test cases. Conclusion RGA has better optimization ability than the conventional algorithms, especially for large-scale and highly complex programs.

Based on the analysis on the characteristics of embedded system based on CAN bus, this paper proposed one CAN bus-oriented test system scheme. The scheme consists of a non-real time host and a real time target; the non-real time host constructs test data, while the real time target drives the data transmission, processes the response of tested system, and returns the results to the non-real time host for analyzing. The system can test, analyze and evaluate the CAN bus-oriented embedded application in a real time, closed-loop and nonintrusive way.