arXiv subjects

Farn Wang

Publications and source records attributed to Farn Wang.

17 recordsLinked to original sources

arXiv2026

SemLink: A Semantic-Aware Automated Test Oracle for Hyperlink Verification using Siamese Sentence-BERT

Web applications rely heavily on hyperlinks to connect disparate information resources. However, the dynamic nature of the web leads to link rot, where targets become unavailable, and more insidiously, semantic drift, where a valid HTTP 200 connection exists, but the target content no longer aligns with the source context. Traditional verification tools, which primarily function as crash oracles by checking HTTP status codes, often fail to detect semantic inconsistencies, thereby compromising web integrity and user experience. While Large Language Models (LLMs) offer semantic understanding, they suffer from high latency, privacy concerns, and prohibitive costs for large-scale regression testing. In this paper, we propose SemLink, a novel automated test oracle for semantic hyperlink verification. SemLink leverages a Siamese Neural Network architecture powered by a pre-trained Sentence-BERT (SBERT) backbone to compute the semantic coherence between a hyperlink's source context (anchor text, surrounding DOM elements, and visual features) and its target page content. To train and evaluate our model, we introduce the Hyperlink-Webpage Positive Pairs (HWPPs) dataset, a rigorously constructed corpus of over 60,000 semantic pairs. Our evaluation demonstrates that SemLink achieves a Recall of 96.00%, comparable to state-of-the-art LLMs (GPT-5.2), while operating approximately 47.5 times faster and requiring significantly fewer computational resources. This work bridges the gap between traditional syntactic checkers and expensive generative AI, offering a robust and efficient solution for automated web quality assurance.

cs.SE↗

arXiv2025

Taming Silent Failures: A Framework for Verifiable AI Reliability

The integration of Artificial Intelligence (AI) into safety-critical systems introduces a new reliability paradigm: silent failures, where AI produces confident but incorrect outputs that can be dangerous. This paper introduces the Formal Assurance and Monitoring Environment (FAME), a novel framework that confronts this challenge. FAME synergizes the mathematical rigor of offline formal synthesis with the vigilance of online runtime monitoring to create a verifiable safety net around opaque AI components. We demonstrate its efficacy in an autonomous vehicle perception system, where FAME successfully detected 93.5% of critical safety violations that were otherwise silent. By contextualizing our framework within the ISO 26262 and ISO/PAS 8800 standards, we provide reliability engineers with a practical, certifiable pathway for deploying trustworthy AI. FAME represents a crucial shift from accepting probabilistic performance to enforcing provable safety in next-generation systems.

cs.SE↗

arXiv2025

GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics

Consumer electronics (CE) connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vulnerabilities allow attackers to exploit CE for broader system attacks while enabling the propagation of malicious code across the CE network, resulting in device failures. Existing deep learning-based traffic anomaly detection systems exhibit high accuracy in traditional network environments but are often overly complex and reliant on static infrastructure, necessitating manual configuration and management. To address these limitations, we propose a scalable network model that integrates Software-defined Networking (SDN) and Compute First Networking (CFN) for next-generation CE networks. In this network model, we propose a Graph Neural Networks-based Network Anomaly Detection framework (GNN-NAD) that integrates SDN-based CE networks and enables the CFN architecture. GNN-NAD uniquely fuses a static, vulnerability-aware attack graph with dynamic traffic features, providing a holistic view of network security. The core of the framework is a GNN model (GSAGE) for graph representation learning, followed by a Random Forest (RF) classifier. This design (GSAGE+RF) demonstrates superior performance compared to existing feature selection methods. Experimental evaluations on CE environment reveal that GNN-NAD achieves superior metrics in accuracy, recall, precision, and F1 score, even with small sample sizes, exceeding the performance of current network anomaly detection methods. This work advances the security and efficiency of next-generation intelligent CE networks.

cs.CR↗

arXiv2025

TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure.

cs.SE↗

arXiv2025

Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard

The Internet of Energy (IoE) integrates IoT-driven digital communication with power grids to enable efficient and sustainable energy systems. Still, its interconnectivity exposes critical infrastructure to sophisticated cyber threats, including adversarial attacks designed to bypass traditional safeguards. Unlike general IoT risks, IoE threats have heightened public safety consequences, demanding resilient solutions. From the networking-level safeguard perspective, we propose a Graph Structure Learning (GSL)-based safeguards framework that jointly optimizes graph topology and node representations to resist adversarial network model manipulation inherently. Through a conceptual overview, architectural discussion, and case study on a security dataset, we demonstrate GSL's superior robustness over representative methods, offering practitioners a viable path to secure IoE networks against evolving attacks. This work highlights the potential of GSL to enhance the resilience and reliability of future IoE networks for practitioners managing critical infrastructure. Lastly, we identify key open challenges and propose future research directions in this novel research area.

cs.CR↗

arXiv2024

Automated Vulnerability Detection Using Deep Learning Technique

Our work explores the utilization of deep learning, specifically leveraging the CodeBERT model, to enhance code security testing for Python applications by detecting SQL injection vulnerabilities. Unlike traditional security testing methods that may be slow and error-prone, our approach transforms source code into vector representations and trains a Long Short-Term Memory (LSTM) model to identify vulnerable patterns. When compared with existing static application security testing (SAST) tools, our model displays superior performance, achieving higher precision, recall, and F1-score. The study demonstrates that deep learning techniques, particularly with CodeBERT's advanced contextual understanding, can significantly improve vulnerability detection, presenting a scalable methodology applicable to various programming languages and vulnerability types.

cs.CR↗

arXiv2016

Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing

To automatically test web applications, crawling-based techniques are usually adopted to mine the behavior models, explore the state spaces or detect the violated invariants of the applications. However, in existing crawlers, rules for identifying the topics of input text fields, such as login ids, passwords, emails, dates and phone numbers, have to be manually configured. Moreover, the rules for one application are very often not suitable for another. In addition, when several rules conflict and match an input text field to more than one topics, it can be difficult to determine which rule suggests a better match. This paper presents a natural-language approach to automatically identify the topics of encountered input fields during crawling by semantically comparing their similarities with the input fields in labeled corpus. In our evaluation with 100 real-world forms, the proposed approach demonstrated comparable performance to the rule-based one. Our experiments also show that the accuracy of the rule-based approach can be improved by up to 19% when integrated with our approach.

cs.SE↗

arXiv2015

PAC Learning-Based Verification and Model Synthesis

We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning a regular model of the set of feasible paths in a program, and testing whether this model contains an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of probably approximately correct (PAC) learning, which uses sampling instead and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.

cs.SE↗

arXiv2013

Coverage Games for Testing Nondeterministic Systems

Modern software systems may exhibit a nondeterministic behavior due to many unpredictable factors. In this work, we propose the node coverage game, a two player turn-based game played on a finite game graph, as a formalization of the problem to test such systems. Each node in the graph represents a {\em functional equivalence class} of the software under test (SUT). One player, the tester, wants to maximize the node coverage, measured by the number of nodes visited when exploring the game graphs, while his opponent, the SUT, wants to minimize it. An optimal test would maximize the cover, and it is an interesting problem to find the maximal number of nodes that the tester can guarantee to visit, irrespective of the responses of the SUT. We show that the decision problem of whether the guarantee is less than a given number is NP-complete. Then we present techniques for testing nondeterministic SUTs with existing test suites for deterministic models. Finally, we report our implementation and experiments.

cs.SE↗

arXiv2012

Rapid Recovery for Systems with Scarce Faults

Our goal is to achieve a high degree of fault tolerance through the control of a safety critical systems. This reduces to solving a game between a malicious environment that injects failures and a controller who tries to establish a correct behavior. We suggest a new control objective for such systems that offers a better balance between complexity and precision: we seek systems that are k-resilient. In order to be k-resilient, a system needs to be able to rapidly recover from a small number, up to k, of local faults infinitely many times, provided that blocks of up to k faults are separated by short recovery periods in which no fault occurs. k-resilience is a simple but powerful abstraction from the precise distribution of local faults, but much more refined than the traditional objective to maximize the number of local faults. We argue why we believe this to be the right level of abstraction for safety critical systems when local faults are few and far between. We show that the computational complexity of constructing optimal control with respect to resilience is low and demonstrate the feasibility through an implementation and experimental results.

eess.SY↗

arXiv2010

Simulation-Checking of Real-Time Systems with Fairness Assumptions

We investigate the simulation problem in of dense-time system. A specification simulates a model if the specification can match every transition that the model can make at a time point. We also adapt the approach of Emerson and Lei and allow for multiple strong and weak fairness assumptions in checking the simulation relation. Furthermore, we allow for fairness assumptions specified as either state-predicates or event-predicates. We focus on a subclass of the problem with at most one fairness assumption for the specification. We then present a simulation-checking algorithm for this subclass. We propose simulation of a model by a specification against a common environment. We present efficient techniques for such simulations to take the common environment into consideration. Our experiment shows that such a consideration can dramatically improve the efficiency of checking simulation. We also report the performance of our algorithm in checking the liveness properties with fairness assumptions.

cs.LO↗

arXiv2006

Symbolic Simulation-Checking of Dense-Time Systems

Intuitively, an (implementation) automata is simulated by a (specification) automata if every externally observable transition by the implementation automata can also be made by the specification automata. In this work, we present a symbolic algorithm for the simulation-checking of timed automatas. We first present a simulation-checking procedure that operates on state spaces, representable with convex polyhedra, of timed automatas. We then present techniques to represent those intermediate result convex polyhedra with zones and make the procedure an algorithm. We then discuss how to handle Zeno states in the implementation automata. Finally, we have endeavored to realize the algorithm and report the performance of our algorithm in the experiment.

cs.LO↗

arXiv2005

Under-approximation of the Greatest Fixpoint in Real-Time System Verification

Techniques for the efficient successive under-approximation of the greatest fixpoint in TCTL formulas can be useful in fast refutation of inevitability properties and vacuity checking. We first give an integrated algorithmic framework for both under and over-approximate model-checking. We design the {\em NZF (Non-Zeno Fairness) predicate}, with a greatest fixpoint formulation, as a unified framework for the evaluation of formulas like $\exists\pfrrη_1$, $\exists\pfrr\pevtη_1$, and $\exists\pevt\pfrrη_1$. We then prove the correctness of a new formulation for the characterization of the NZF predicate based on zone search and the least fixpoint evaluation. The new formulation then leads to the design of an evaluation algorithm, with the capability of successive under-approximation, for $\exists\pfrrη_1$, $\exists\pfrr\pevtη_1$, and $\exists\pevt\pfrrη_1$. We then present techniques to efficiently search for the zones and to speed up the under-approximate evaluation of those three formulas. Our experiments show that the techniques have significantly enhanced the verification performance against several benchmarks over exact model-checking.

cs.SE↗

arXiv2005

Under-approximation of the Greatest Fixpoints in Real-Time System Verification

cs.SE↗

arXiv2003

Symbolic Parametric Analysis of Embedded Systems with BDD-like Data-Structures

We use dense variable-ordering to define HRD (Hybrid-Restriction Diagram), a new BDD-like data-structure for the representation and manipulation of state-spaces of linear hybrid automata. We present and discuss various manipulation algorithms for HRD, including the basic set-oriented operations, weakest precondition calculation, and normalization. We implemented the ideas and experimented to see their performance. Finally, we have also developed a pruning technique for state-space exploration based on parameter valuation space characterization. The technique showed good promise in our experiment.

cs.DS↗

arXiv2003

TCTL Inevitability Analysis of Dense-time Systems

Inevitability properties in branching temporal logics are of the syntax forall eventually ϕ, where ϕis an arbitrary (timed) CTL formula. In the sense that "good things will happen", they are parallel to the "liveness" properties in linear temporal logics. Such inevitability properties in dense-time logics can be analyzed with greatest fixpoint calculation. We present algorithms to model-check inevitability properties both with and without requirement of non-Zeno computations. We discuss a technique for early decision on greatest fixpoints in the temporal logics, and experiment with the effect of non-Zeno computations on the evaluation of greatest fixpoints. We also discuss the TCTL subclass with only universal path quantifiers which allows for the safe abstraction analysis of inevitability properties. Finally, we report our implementation and experiments to show the plausibility of our ideas.

cs.SC↗

arXiv2003

Numerical Coverage Estimation for the Symbolic Simulation of Real-Time Systems

Three numerical coverage metrics for the symbolic simulation of dense-time systems and their estimation methods are presented. Special techniques to derive numerical estimations of dense-time state-spaces have also been developed. Properties of the metrics are also discussed with respect to four criteria. Implementation and experiments are then reported.

cs.SE↗