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Software failures caused by data race bugs have always been major concerns in parallel and distributed systems, despite significant efforts spent in software testing. Due to their nondeterministic and hard-to-reproduce features, when evaluating systems’ operational reliability, a rather long period of experimental execution time is expected to be spent on observing failures caused by data race conditions

This paper analyzes security problems of modern computer systems caused by vulnerabilities in their operating systems (OSs). Our scrutiny of widely used enterprise OSs focuses on their vulnerabilities by examining the statistical data available on how vulnerabilities in these systems are disclosed and eliminated, and by assessing their criticality. This is done by using statistics from both the National

Software defect prediction (SDP) is an available way to enhance test efficiency and guarantee software reliability. However, there are more clean instances than defective instances in real software projects, and this results in severe class distribution skews and gets the poor performance of classifiers. So solving the class-imbalance problem in SDP has attracted growing attention from industry and

With the rapid development of smart devices, mobile phones have permeated many aspects of our life. Unfortunately, their widespread popularization attracted endless attacks that are serious threats for users. As the mobile system with the largest market share, Android has already become the hardest hit for years. To Detect Android Malware by ORGB Anlysis, in this paper, we present DAMBA, a novel prototype

Security vulnerability prediction (SVP) can identify potential vulnerable modules in advance and then help developers to allocate most of the test resources to these modules. To evaluate the performance of different SVP methods, we should take the security audit and code inspection into account and then consider effort-aware performance measures (such as $ACC$ and $P_{\rm opt}$ ). However, to the best

Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. In our developer survey, commenting has become an important, yet often-neglected activity when programming. Moreover, there is a lack of formal and automatic way in current practice to remind developers where to comment in the source code. To provide informative guidance on

Cyber-physical software delivers context-aware services through continually interacting with its physical environment and adapting to the changing surroundings. However, when the software's assumptions on the environment no longer hold, the interactions can introduce errors for leading to unexpected behaviors and even system failures. One promising solution to this problem is to conduct runtime monitoring

The rapid growth of Android malware results in a large body of approaches devoted to malware analysis by leveraging machine learning algorithms. However, the effectiveness of these approaches primarily depends on the manual feature engineering process, which is time-consuming and labor-intensive based on expert knowledge and intuition. In this paper, we propose an automatic approach that engineers

Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results

Requirements traceability is broadly recognized as a critical element of any rigorous software development process, especially for building safety-critical software (SCS) systems. Model-driven development (MDD) is increasingly used to develop SCS in many domains, such as automotive and aerospace. MDD provides new opportunities for establishing traceability links through modeling and model transformations

Malware detection is an important and challenging issue in the Android ecosystem. Many approaches have been proposed to distinguish malicious applications from benign ones, but few of them can represent the behavior patterns of malicious applications and help understand their intention. In this paper, we propose LSCDroid, a malware detecting approach that cannot only detect malware but also help understand

SQL injection attack (SQLIA) is among the most common security threats to web-based services that are deployed on cloud. By exploiting web software vulnerabilities, SQL injection attackers can run arbitrary malicious code on target databases to acquire or compromise sensitive data. Although web application firewalls (WAFs) are offered by most cloud service providers, tenants are reluctant to pay for

Numerical computation is often involved in software of embedded control systems, cyber-physical systems, artificial neural network systems, big data processing systems, etc. Automatically discovering numerical loop invariants is fundamental for checking the safety of such software. Abstract interpretation provides a framework to automatically discover sound invariants but which may be not precise enough

Prediction of event such as failure event or critical warning event plays an important role in reliability engineering. In this paper, we present a semiparametric method to predict event occurrences for an individual unit in real time using recurrent event data. The intensity of event occurrence is modeled using the extended Cox proportional-hazard model and the distinction of units is achieved using

In this paper, the new generalized inflection S-shaped software reliability growth model is proposed. It is a very flexible finite failure Poisson process that possesses two distinguishing features: 1) includes as special cases the popular inflection S-shaped model, Goel generalized nonhomogenous Poisson process, and Goel–Okumoto model and 2) differently than these latter models, allows for modeling

This paper develops an improved dynamic reliability model for compressor rotor system, defined as a “unconventional active system” here. First, regarding to the variety of system-component connections, system specific reliability modeling technique is presented, in which the stress and strength are both nonnegative stochastic processes. The new model fully considers the load characteristics of rotor

The global reliability sensitivity index (GRSI) can measure the effect of model input variable on the failure probability of the structure and provide guidance for the reliability-based design optimization. In this paper, to efficiently estimate the GRSI, an equivalent form of the GRSI is derived by elaborative combination of Bayes’ theorem and the law of total expectation in the successive intervals

The reliability assessment for a system with multiple prior information can be challenging. In this paper, we propose a novel Bayesian melding (BM) approach to combine the inconsistent prior distributions. This approach is first investigated to address a flaw in traditional BM methods and then extended to a general multilevel hierarchical system with biased priors and limited data. We realize our method

Dependability and performance analysis of modern systems is facing great challenges: their scale is growing, they are becoming massively distributed, interconnected, and evolving. Such complexity makes model-based assessment a difficult and time-consuming task. For the evaluation of large systems, reusable submodels are typically adopted as an effective way to address the complexity and to improve

With the rapid development of technology scaling and cell density improvement for capacity increase and cost reduction, nand flash memory is confronted with degraded reliability. On one hand, while low-density parity-check (LDPC) codes have been deployed in today's nand flash memories to enhance reliability, flash read latency has still been a performance bottleneck with the increased raw bit error

To cope with the control problem of industrial processes with partial actuator failure and disturbance, an improved synthetic minmax optimization design-based ${H_{\infty} }$ linear quadratic (LQ) tracking control strategy is presented in this paper. A new state space model that integrates the dynamics of the process states and the tracking error is first formulated as the basic dynamic process representation

Mutation testing is a fault-oriented software testing technique, and a test suite generated based on the criterion of mutation testing generally has a high capability in detecting faults. A mutant that is hard killed is called a stubborn one. The traditional methods of test data generation often fail to generate test data that kill stubborn mutants. To improve the efficiency of killing stubborn mutants

Abstract test cases (ATCs) have been widely used in practice, including in combinatorial testing and in software product line testing. When constructing a set of ATCs, due to limited testing resources in practice (e.g., in regression testing), test case prioritization (TCP) has been proposed to improve the testing quality, aiming at ordering test cases to increase the speed with which faults are detected

For a long time, the machinery's reliability allocation methods have followed the electronics', and thus all of the interactions among moving components and parts in machines are neglected. In fact, the allocated results are not reasonable for mechanical products; even the allocation granularity is not appropriate to guide the reliability design. To overcome these problems, this paper proposes an allocation

Wireless sensor networks (WSNs) find application in various fields like environmental monitoring, health-care, land security, and many more. To ease our day-to-day activity, WSNs have become an integral tool for complex data gathering tasks. Monitoring a phenomenon by a WSN depends on the collective data provided by the sensor nodes. To ensure reliable operation of WSNs, it is important to quantify

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions

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Cryptographic hash functions are security-critical algorithms with many practical applications, notably in digital signatures. Developing an approach to test them can be particularly difficult, and bugs can remain unnoticed for many years. We revisit the NIST hash function competition, which was used to develop the SHA-3 standard, and apply a new testing strategy to all available reference implementations