Serializability (SER) and snapshot isolation (SI) are widely used transactional isolation levels in database systems. The isolation checking problem asks whether a given execution history of a database system satisfies a specified isolation level. However, existing SER and SI checkers, whether traditional black-box checkers or recent timestamp-based white-box ones, operate offline and require the entire history to be available to construct a dependency graph, making them unsuitable for continuous and ever-growing histories. This paper addresses online isolation checking by extending the timestamp-based isolation checking approach to online settings. Specifically, we design Chronos, an efficient timestamp-based offline SI checker. Chronos is incremental and avoids constructing a start-ordered serialization graph for the entire history, making it well-suited for online scenarios. We further extend Chronos into an online SI checker, Aion, addressing several key challenges unique to online settings. Additionally, we develop Aion-SER for online SER checking. Experiments highlight that Chronos processes offline histories with up to one million transactions in seconds, greatly outperforming existing SI checkers. Furthermore, Aion and Aion-ser sustain a throughput of approximately 12K transactions per second, demonstrating their practicality for online isolation checking.

With the development of Deep Learning, Natural Language Processing (NLP) applications have reached or even exceeded human-level capabilities in certain tasks. Although NLP applications have shown good performance, they can still have bugs like traditional software and even lead to serious consequences. Inspired by Lego blocks and syntax structure analysis, we propose an assembling test generation method for NLP applications or models and implement it in NLPLego . The key idea of NLPLego is to assemble the sentence skeleton and adjuncts in order by simulating the building of Lego blocks to generate multiple grammatically and semantically correct sentences based on one seed sentence. The sentences generated by NLPLego have derivation relations and different degrees of variation. These characteristics make it well-suited for integration with metamorphic testing theory, addressing the challenge of test oracle absence in NLP application testing. To validate NLPLego , we conduct experiments on three commonly used NLP tasks (i.e., machine reading comprehension, sentiment analysis, and semantic similarity measures), focusing on the efficiency of test generation and the quality and effectiveness of generated tests. We select five advanced NLP models and one popular industrial NLP software as the tested subjects. Given seed tests from SQuAD 2.0, SST, and QQP, NLPLego successfully detects 1,732, 3,140, and 261,879 incorrect behaviors with around 93.1% precision in three tasks, respectively. The experiment results show that NLPLego can efficiently generate high-quality tests for multiple NLP tasks to detect erroneous behaviors effectively. In the case study, we analyze the testing results provided by NLPLego to obtain intuitive representations of the different NLP capabilities of the tested subjects. The case study confirms that NLPLego can provide developers with clarity on the direction to improve NLP models or applications, laying the foundation for enhancing performance.

The use of a swarm of low-cost, mission-specific drones to form a Flying Ad-hoc Network (FANET) has literally become a ’hotspot’ in the drone community. A number of studies have been conducted on how to achieve a FANET, but few have considered the security perspectives of this subject. FANET’s unique features have made it difficult to strengthen its defense against ever-changing security threats. Today, more and more FANET applications are implemented into civil airspace, but the development of FANET security has remained unsatisfactory. In this paper, we try to address this issue by proposing a new Intrusion Detection System (IDS), an hybrid method based on both spectral traffic analysis and a robust controller / observer for anomaly estimation inside UAV networks. The proposed hybrid method considers, as a preliminary step, a statistical signature of the traffic exchanged in the network. By examining the resulted signatures, the differences are used to select the accurate model for accurate estimation of that abnormal traffic. The proposed IDS design has been successfully applied to some relevant practical problems such as ad hoc networks for aerial vehicles, and the effectiveness is illustrated by using real traffic traces including Distributed Denial of Service (DDoS) attacks. Our first results show promising perspectives for Intrusion Detection System (IDS) in UAV communication networks. Indeed, different types of anomaly have been considered and they are all accurately detected by the intrusion detection process we propose in this paper. Finally, both simulation-based validation and real-time real-world based implementation of our IDS are described in this article.