This article studies the fault-tolerant consensus problem with the guaranteed transient performance of multiagent systems (MASs) subject to unknown time-varying actuator faults and disturbances. The general actuator faults, including both multiplicative and additive time-varying faults, are considered in such a problem for the first time. Both single-integrator modeled agents and double-integrator modeled agents are investigated. The transient performance is ensured in the sense that position errors between each pair of neighboring agents are guaranteed within certain user-defined time-varying performance bounds. Adaptive laws are designed to estimate information about faults and disturbances. For MASs with additive faults, the proposed controllers ensure errors asymptotically converge to zero with guaranteed transient performance. For MASs with both multiplicative faults and additive faults, the proposed controllers ensure errors converge to a residual set without asymptotic convergence but still with guaranteed transient performance. Two simulation examples are provided to evaluate the proposed schemes.

In this article, an adaptive event-triggered fault-tolerant asymptotic tracking control problem guaranteeing prescribed performance is addressed for a class of block-triangular multi-input and multioutput uncertain nonlinear systems with unknown nonlinearities, unknown control directions, and actuator faults. Through a systematic co-design of the adaptive control law and the event-triggered mechanism, including fixed and relative threshold strategies, a control scheme with low structure and calculation complexity is designed to conserve system communication and computation resources. In this design, the output asymptotic tracking is achieved. The Nussbaum gain technique is incorporated to overcome unknown control directions with a new adaptive law, and a type of barrier Lyapunov function is adopted to handle the prescribed performance control problem, which contributes to a novel control law with strong robustness. The robust controller can address the uncertainties and couplings derived from the system structure, actuator faults, and event-triggered rules, without using approximating structures or compensators. Besides, the explosion of complexity is avoided. It is proved that all signals of the closed-loop system remain bounded, and system tracking errors asymptotically approach 0 with the prescribed performance, while the Zeno behavior is prevented. Finally, the effectiveness of the proposed control scheme is evaluated via an application example of the half-car active suspension system.

Abstract Security vulnerability prediction (SVP) can construct models to identify potentially vulnerable program modules via machine learning. Two kinds of features from different points of view are used to measure the extracted modules in previous studies. One kind considers traditional software metrics as features, and the other kind uses text mining to extract term vectors as features. Therefore, gathered SVP data sets often have numerous features and result in the curse of dimensionality. In this article, we mainly investigate the impact of filter‐based ranking feature selection (FRFS) methods on SVP, since other types of feature selection methods have too much computational cost. In empirical studies, we first consider three real‐world large‐scale web applications. Then we consider seven methods from three FRFS categories for FRFS and use a random forest classifier to construct SVP models. Final results show that given the similar code inspection cost, using FRFS can improve the performance of SVP when compared with state‐of‐the‐art baselines. Moreover, we use McNemar's test to perform diversity analysis on identified vulnerable modules by using different FRFS methods, and we are surprised to find that almost all the FRFS methods can identify similar vulnerable modules via diversity analysis.

Informal discussions on social platforms (e.g., Stack Overflow, CodeProject) have accumulated a large body of programming knowledge in the form of natural language text. Natural language process (NLP) techniques can be utilized to harvest this knowledge base for software engineering tasks. However, consistent vocabulary for a concept is essential to make an effective use of these NLP techniques. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms (such as abbreviations, synonyms and misspellings) in informal discussions. Existing techniques to deal with such morphological forms are either designed for general English or mainly resort to domain-specific lexical rules. A thesaurus, which contains software-specific terms and commonly-used morphological forms, is desirable to perform normalization for software engineering text. However, constructing this thesaurus in a manual way is a challenge task. In this paper, we propose an automatic unsupervised approach to build such a thesaurus. In particular, we first identify software-specific terms by utilizing a software-specific corpus (e.g., Stack Overflow) and a general corpus (e.g., Wikipedia). Then we infer morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations. Finally, we perform graph analysis on morphological relations. We evaluate the coverage and accuracy of our constructed thesaurus against community-cumulated lists of software-specific terms, abbreviations and synonyms. We also manually examine the correctness of the identified abbreviations and synonyms in our thesaurus. We demonstrate the usefulness of our constructed thesaurus by developing three applications and also verify the generality of our approach in constructing thesauruses from data sources in other domains.

This article is devoted to the event-triggered adaptive control design for uncertain nonlinear systems with full state constraints. A robust adaptive control method enabling the codesign of event-triggering mechanism is proposed, in which the communication burden between controllers and actuators is reduced, and both the physical limitation of the plant with uncertainties and the measurement errors introduced by event-triggering mechanisms can be simultaneously addressed. In addition, a priori knowledge of the signs of unknown virtual control coefficients is not required in the presented controller design methods. Furthermore, Lyapunov stability analysis guarantees that all states in the closed-loop nonlinear system are bounded, the state constraints are not violated, and the tracking errors are driven to a compact set. Finally, a designed example is given to illustrate the effectiveness and advantages of the presented design approaches.