arXiv subjects

Tao Yue

Publications and source records attributed to Tao Yue.

At least 37 records · Page 2Linked to original sources

arXiv2024

HITA: An Architecture for System-level Testing of Healthcare IoT Applications

System-level testing of healthcare Internet of Things (IoT) applications requires creating a test infrastructure with integrated medical devices and third-party applications. A significant challenge in creating such test infrastructure is that healthcare IoT applications evolve continuously with the addition of new medical devices from different vendors and new services offered by different third-party organizations following different architectures. Moreover, creating test infrastructure with a large number of different types of medical devices is time-consuming, financially expensive, and practically infeasible. Oslo City's healthcare department faced these challenges while working with various healthcare IoT applications. To address these challenges, this paper presents a real-world test infrastructure software architecture (HITA) designed for healthcare IoT applications. We evaluated HITA's digital twin (DT) generation component implemented using model-based and machine learning (ML) approaches in terms of DT fidelity, scalability, and time cost of generating DTs. Results show that the fidelity of DTs created using model-based and ML approaches reach 94% and 95%, respectively. Results from operating 100 DTs concurrently show that the DT generation component is scalable and ML-based DTs have a higher time cost.

cs.SE↗

arXiv2024

A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.

cs.SE↗

arXiv2024

Evaluating Search-Based Software Microbenchmark Prioritization

Ensuring that software performance does not degrade after a code change is paramount. A solution is to regularly execute software microbenchmarks, a performance testing technique similar to (functional) unit tests, which, however, often becomes infeasible due to extensive runtimes. To address that challenge, research has investigated regression testing techniques, such as test case prioritization (TCP), which reorder the execution within a microbenchmark suite to detect larger performance changes sooner. Such techniques are either designed for unit tests and perform sub-par on microbenchmarks or require complex performance models, drastically reducing their potential application. In this paper, we empirically evaluate single- and multi-objective search-based microbenchmark prioritization techniques to understand whether they are more effective and efficient than greedy, coverage-based techniques. For this, we devise three search objectives, i.e., coverage to maximize, coverage overlap to minimize, and historical performance change detection to maximize. We find that search algorithms (SAs) are only competitive with but do not outperform the best greedy, coverage-based baselines. However, a simple greedy technique utilizing solely the performance change history (without coverage information) is equally or more effective than the best coverage-based techniques while being considerably more efficient, with a runtime overhead of less than 1%. These results show that simple, non-coverage-based techniques are a better fit for microbenchmarks than complex coverage-based techniques.

cs.SE↗

arXiv2024

Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-physical Systems

Cyber-Physical Systems (CPSs), e.g., elevator systems and autonomous driving systems, are progressively permeating our everyday lives. To ensure their safety, various analyses need to be conducted, such as anomaly detection and time-to-event analysis (the focus of this paper). Recently, it has been widely accepted that digital Twins (DTs) can serve as an efficient method to aid in the development, maintenance, and safe and secure operation of CPSs. However, CPSs frequently evolve, e.g., with new or updated functionalities, which demand their corresponding DTs be co-evolved, i.e., in synchronization with the CPSs. To that end, we propose a novel method, named PPT, utilizing an uncertainty-aware transfer learning for DT evolution. Specifically, we first pretrain PPT with a pretraining dataset to acquire generic knowledge about the CPSs, followed by adapting it to a specific CPS with the help of prompt tuning. Results highlight that PPT is effective in time-to-event analysis in both elevator and ADSs case studies, on average, outperforming a baseline method by 7.31 and 12.58 in terms of Huber loss, respectively. The experiment results also affirm the effectiveness of transfer learning, prompt tuning and uncertainty quantification in terms of reducing Huber loss by at least 21.32, 3.14 and 4.08, respectively, in both case studies.

cs.SE↗

arXiv2024

Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as text generation, summarization, and classification. Given that such models are trained on a humongous amount of online knowledge, we hypothesize that LLMs can assess whether driving scenarios generated by autonomous driving testing techniques are realistic, i.e., being aligned with real-world driving conditions. To test this hypothesis, we conducted an empirical evaluation to assess whether LLMs are effective and robust in performing the task. This reality check is an important step towards devising LLM-based autonomous driving testing techniques. For our empirical evaluation, we selected 64 realistic scenarios from \deepscenario--an open driving scenario dataset. Next, by introducing minor changes to them, we created 512 additional realistic scenarios, to form an overall dataset of 576 scenarios. With this dataset, we evaluated three LLMs (\gpt, \llama, and \mistral) to assess their robustness in assessing the realism of driving scenarios. Our results show that: (1) Overall, \gpt achieved the highest robustness compared to \llama and \mistral, consistently throughout almost all scenarios, roads, and weather conditions; (2) \mistral performed the worst consistently; (3) \llama achieved good results under certain conditions; and (4) roads and weather conditions do influence the robustness of the LLMs.

cs.SE↗

arXiv2024

Mitigating Noise in Quantum Software Testing Using Machine Learning

Quantum Computing (QC) promises computational speedup over classic computing for solving complex problems. However, noise exists in current and near-term quantum computers. Quantum software testing (for gaining confidence in quantum software's correctness) is inevitably impacted by noise, to the extent that it is impossible to know if a test case failed due to noise or real faults. Existing testing techniques test quantum programs without considering noise, i.e., by executing tests on ideal quantum computer simulators. Consequently, they are not directly applicable to testing quantum software on real quantum computers or noisy simulators. To this end, we propose a noise-aware approach (named QOIN) to alleviate the noise effect on test results of quantum programs. QOIN employs machine learning techniques (e.g., transfer learning) to learn the noise effect of a quantum computer and filter it from a quantum program's outputs. Such filtered outputs are then used as the input to perform test case assessments (determining the passing or failing of a test case execution against a test oracle). We evaluated QOIN on IBM's 23 noise models, Google's two available noise models, and Rigetti's Quantum Virtual Machine (QVM), with nine real-world quantum programs and 1000 artificial quantum programs. Results show that QOIN can reduce the noise effect by more than $80\%$ on the majority of noise models. For quantum software testing, we used an existing test oracle and showed that QOIN attained scores of $99\%$, $75\%$, and $86\%$ for precision, recall, and F1-score, respectively, for the test oracle across six real-world programs. For artificial programs, QOIN achieved scores of $93\%$, $79\%$, and $86\%$ for precision, recall, and F1-score. This highlights QOIN's effectiveness in learning noise patterns for noise-aware quantum software testing.

cs.SE↗

arXiv2023

Model-based Digital Twins of Medicine Dispensers for Healthcare IoT Applications

Healthcare applications with the Internet of Things (IoT) are often safety-critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System-level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary-wise expensive. Moreover, applications continuously evolve, e.g., introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model-based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.

cs.SE↗

arXiv2023

EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular, EpiTESTER adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV, EpiTESTER benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors and then calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER, we compare it with a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented) and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four initial environments from CARLA, an open-source simulator for autonomous driving research, and an end-to-end AV controller, Interfuser. Our results show that EpiTESTER achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.

cs.SE↗

arXiv2023

Safety Assessment of Vehicle Characteristics Variations in Autonomous Driving Systems

Autonomous driving systems (ADSs) must be sufficiently tested to ensure their safety. Though various ADS testing methods have shown promising results, they are limited to a fixed set of vehicle characteristics settings (VCSs). The impact of variations in vehicle characteristics (e.g., mass, tire friction) on the safety of ADSs has not been sufficiently and systematically studied.Such variations are often due to wear and tear, production errors, etc., which may lead to unexpected driving behaviours of ADSs. To this end, in this paper, we propose a method, named SAFEVAR, to systematically find minimum variations to the original vehicle characteristics setting, which affect the safety of the ADS deployed on the vehicle. To evaluate the effectiveness of SAFEVAR, we employed two ADSs and conducted experiments with two driving simulators. Results show that SAFEVAR, equipped with NSGA-II, generates more critical VCSs that put the vehicle into unsafe situations, as compared with two baseline algorithms: Random Search and a mutation-based fuzzer. We also identified critical vehicle characteristics and reported to which extent varying their settings put the ADS vehicles in unsafe situations.

cs.SE↗

arXiv2023

Uncertainty-Aware Test Prioritization: Approaches and Empirical Evaluation

Complex software systems, e.g., Cyber-Physical Systems (CPSs), interact with the real world; thus, they often behave unexpectedly in uncertain environments. Testing such systems is challenging due to limited resources, time, complex testing infrastructure setup, and the inherent uncertainties in their operating environment. Devising uncertainty-aware testing solutions supported with test optimization techniques can be considered as a mandate for tackling this challenge. This paper proposes an uncertainty-aware and time-aware test case prioritization approach, named UncerPrio, for optimizing a sequence of tests to execute with a multi-objective search. To guide the prioritization with uncertainty, we identify four uncertainty measures: uncertainty measurement (AUM), uncertainty space (PUS), the number of uncertainties (ANU), and uncertainty coverage (PUU). Based on these measures and their combinations, we proposed 10 uncertainty-aware and multi-objective test case prioritization problems, and each problem was additionally defined with one cost objective (execution cost, PET) to be minimized and one effective measure (model coverage, PTR) to be maximized. Moreover, considering time constraints for test executions (i.e., time-aware), we defined 10 time budgets for all the 10 problems for identifying the best strategy in solving uncertainty-aware test prioritization. In our empirical study, we employed four well-known Multi-Objective Search Algorithms (MuOSAs): NSGA-II, MOCell, SPEA2, and CellDE with five use cases from two industrial CPS subject systems, and used Random Algorithm (RS) as the comparison baseline. Results show that all the MuOSAs significantly outperformed RS. The strategy of Prob.6 f(PET,PTR,AUM,ANU) (i.e., the problem with uncertainty measures AUM and ANU combined) achieved the overall best performance in observing uncertainty when using 100% time budget.

cs.SE↗

arXiv2023

Automated Test Generation for Medical Rules Web Services: A Case Study at the Cancer Registry of Norway

The Cancer Registry of Norway (CRN) collects, curates, and manages data related to cancer patients in Norway, supported by an interactive, human-in-the-loop, socio-technical decision support software system. Automated software testing of this software system is inevitable; however, currently, it is limited in CRN's practice. To this end, we present an industrial case study to evaluate an AI-based system-level testing tool, i.e., EvoMaster, in terms of its effectiveness in testing CRN's software system. In particular, we focus on GURI, CRN's medical rule engine, which is a key component at the CRN. We test GURI with EvoMaster's black-box and white-box tools and study their test effectiveness regarding code coverage, errors found, and domain-specific rule coverage. The results show that all EvoMaster tools achieve a similar code coverage; i.e., around 19% line, 13% branch, and 20% method; and find a similar number of errors; i.e., 1 in GURI's code. Concerning domain-specific coverage, EvoMaster's black-box tool is the most effective in generating tests that lead to applied rules; i.e., 100% of the aggregation rules and between 12.86% and 25.81% of the validation rules; and to diverse rule execution results; i.e., 86.84% to 89.95% of the aggregation rules and 0.93% to 1.72% of the validation rules pass, and 1.70% to 3.12% of the aggregation rules and 1.58% to 3.74% of the validation rules fail. We further observe that the results are consistent across 10 versions of the rules. Based on these results, we recommend using EvoMaster's black-box tool to test GURI since it provides good results and advances the current state of practice at the CRN. Nonetheless, EvoMaster needs to be extended to employ domain-specific optimization objectives to improve test effectiveness further. Finally, we conclude with lessons learned and potential research directions, which we believe are generally applicable.

cs.SE↗

arXiv2023

C-Silicon-based metasurfaces for aperture-robust spectrometer/imaging with angle integration

Compared with conventional grating-based spectrometers, reconstructive spectrometers based on spectrally engineered filtering have the advantage of miniaturization because of the less demand for dispersive optics and free propagation space. However, available reconstructive spectrometers fail to balance the performance on operational bandwidth, spectral diversity and angular stability. In this work, we proposed a compact silicon metasurfaces based spectrometer/camera. After angle integration, the spectral response of the system is robust to angle/aperture within a wide working bandwidth from 400nm to 800nm. It is experimentally demonstrated that the proposed method could maintain the spectral consistency from F/1.8 to F/4 (The corresponding angle of incident light ranges from 7° to 16°) and the incident hyperspectral signal could be accurately reconstructed with a fidelity exceeding 99%. Additionally, a spectral imaging system with 400x400 pixels is also established in this work. The accurate reconstructed hyperspectral image indicates that the proposed aperture-robust spectrometer has the potential to be extended as a high-resolution broadband hyperspectral camera.

physics.optics↗

arXiv2023

DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data

Autonomous driving systems (ADSs) are capable of sensing the environment and making driving decisions autonomously. These systems are safety-critical, and testing them is one of the important approaches to ensure their safety. However, due to the inherent complexity of ADSs and the high dimensionality of their operating environment, the number of possible test scenarios for ADSs is infinite. Besides, the operating environment of ADSs is dynamic, continuously evolving, and full of uncertainties, which requires a testing approach adaptive to the environment. In addition, existing ADS testing techniques have limited effectiveness in ensuring the realism of test scenarios, especially the realism of weather conditions and their changes over time. Recently, reinforcement learning (RL) has demonstrated great potential in addressing challenging problems, especially those requiring constant adaptations to dynamic environments. To this end, we present DeepQTest, a novel ADS testing approach that uses RL to learn environment configurations with a high chance of revealing abnormal ADS behaviors. Specifically, DeepQTest employs Deep Q-Learning and adopts three safety and comfort measures to construct the reward functions. To ensure the realism of generated scenarios, DeepQTest defines a set of realistic constraints and introduces real-world weather conditions into the simulated environment. We employed three comparison baselines, i.e., random, greedy, and a state-of-the-art RL-based approach DeepCOllision, for evaluating DeepQTest on an industrial-scale ADS. Evaluation results show that DeepQTest demonstrated significantly better effectiveness in terms of generating scenarios leading to collisions and ensuring scenario realism compared with the baselines. In addition, among the three reward functions implemented in DeepQTest, Time-To-Collision is recommended as the best design according to our study.

cs.SE↗

arXiv2023

Cost Reduction on Testing Evolving Cancer Registry System

The Cancer Registration Support System (CaReSS), built by the Cancer Registry of Norway (CRN), is a complex real-world socio-technical software system that undergoes continuous evolution in its implementation. Consequently, continuous testing of CaReSS with automated testing tools is needed such that its dependability is always ensured. Towards automated testing of a key software subsystem of CaReSS, i.e., GURI, we present a real-world application of an extension to the open-source tool EvoMaster, which automatically generates test cases with evolutionary algorithms. We named the extension EvoClass, which enhances EvoMaster with a machine learning classifier to reduce the overall testing cost. This is imperative since testing with EvoMaster involves sending many requests to GURI deployed in different environments, including the production environment, whose performance and functionality could potentially be affected by many requests. The machine learning classifier of EvoClass can predict whether a request generated by EvoMaster will be executed successfully or not; if not, the classifier filters out such requests, consequently reducing the number of requests to be executed on GURI. We evaluated EvoClass on ten GURI versions over four years in three environments: development, testing, and production. Results showed that EvoClass can significantly reduce the testing cost of evolving GURI without reducing testing effectiveness (measured as rule coverage) across all three environments, as compared to the default EvoMaster. Overall, EvoClass achieved ~31% of overall cost reduction. Finally, we report our experiences and lessons learned that are equally valuable for researchers and practitioners.

cs.SE↗

arXiv2023

Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems

Anomaly detection is critical to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of attacks and CPS themselves, anomaly detection in CPS is becoming more and more challenging. In our previous work, we proposed a digital twin-based anomaly detection method, called ATTAIN, which takes advantage of both historical and real-time data of CPS. However, such data vary significantly in terms of difficulty. Therefore, similar to human learning processes, deep learning models (e.g., ATTAIN) can benefit from an easy-to-difficult curriculum. To this end, in this paper, we present a novel approach, named digitaL twin-based Anomaly deTecTion wIth Curriculum lEarning (LATTICE), which extends ATTAIN by introducing curriculum learning to optimize its learning paradigm. LATTICE attributes each sample with a difficulty score, before being fed into a training scheduler. The training scheduler samples batches of training data based on these difficulty scores such that learning from easy to difficult data can be performed. To evaluate LATTICE, we use five publicly available datasets collected from five real-world CPS testbeds. We compare LATTICE with ATTAIN and two other state-of-the-art anomaly detectors. Evaluation results show that LATTICE outperforms the three baselines and ATTAIN by 0.906%-2.367% in terms of the F1 score. LATTICE also, on average, reduces the training time of ATTAIN by 4.2% on the five datasets and is on par with the baselines in terms of detection delay time.

cs.LG↗

arXiv2023

Evolve the Model Universe of a System Universe

Uncertain, unpredictable, real time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash the potential of such systems and facilitate their wider adoption, ensuring the trustworthiness of their decision making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most updated states. Such representations are often in the form of prior knowledge based and machine learning models, together called model universe. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution.

cs.SE↗

arXiv2023

Towards Quantum Software Requirements Engineering

Quantum software engineering (QSE) is receiving increasing attention, as evidenced by increasing publications on topics, e.g., quantum software modeling, testing, and debugging. However, in the literature, quantum software requirements engineering (QSRE) is still a software engineering area that is relatively less investigated. To this end, in this paper, we provide an initial set of thoughts about how requirements engineering for quantum software might differ from that for classical software after making an effort to map classical requirements classifications (e.g., functional and extra-functional requirements) into the context of quantum software. Moreover, we provide discussions on various aspects of QSRE that deserve attention from the quantum software engineering community.

cs.SE↗

arXiv2023

Numerical study on the effects of fluid properties in electrohydrodynamic pulsating jet under constant voltage

Pulsating jet is one of the common working modes in electrohydrodynamic printing (EHDP) that process is highly affected by operating parameters and material properties. In this paper, the processes of pulsating jet for liquids with different physical properties were investigated using numerical simulation. An electrohydrodynamic solver was established, and a charge flux restricting step was adopted to ensure a realistic distribution of free charges. Three various ejection regimes were observed in our simulations: oscillating cone (OC), choked jet (CJ), and stable cone-jet (SJ). We found that three dimensionless numbers relating to liquid properties determined the ejection regime: the Ohnesorge number, Q0εr/Q, and Q0/(QRe). Based on those dimensionless numbers, the roles of liquid properties on pulsating jet (OC and CJ) were analyzed. In OC, the break of the jet is due to the significant oscillation of the Taylor cone, which is mainly affected by viscosity and conductivity. In CJ, the jet emission is terminated by the excessive resistant force in the cone-jet transition region. For liquids with low and medium viscosity, the dominant resistant force is the polarization force or viscous force when εrRe is larger or smaller than 1, respectively. For high viscosity liquids, the viscous force always becomes the major resistance. These results further reveal the physical mechanism of pulsating jet and can be used to guide its application.

physics.flu-dyn↗