Path Coverage Research Articles

Freeform trajectory generation for fiber reinforced polymer composites additive manufacturing with variable-deposition-width

Continuous carbon fiber-reinforced polymers (CCFRPs) printing is a unique additive manufacturing (AM) technique that enhances design flexibility and enables on-demand production of lightweight, high-strength composite parts. However, current trajectory generation methods for CCFRPs-AM have limited control over fiber placement and flexible matrix path infill, particularly in complex regions, often using constant deposition widths that restrict path coverage and mechanical properties. To address these limitations, this paper presents a variable-deposition-width (VDW) trajectory generation method for CCFRPs-AM. The approach integrates a streamline-based technique for fiber paths with a Voronoi diagram-based method for matrix path generation, optimizing path placement based on 2D stress fields while accommodating user-defined fiber paths. Mechanical tests by us on fabricated open-hole composite components demonstrate that the proposed approach enhances the interfacial bonding and increases the maximal loading capacity under tensile stress.

Using a 1D Radially Symmetric Coda Envelope Model for Robust Moment Magnitude (Mw) Estimation in Iraq’s Tectonically Diverse Zones

ABSTRACT Robust estimation of moment magnitude (Mw) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of Mw for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Hence, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.

SRank: Guiding schema selection in NoSQL document stores

The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores.

A DDoS Tracking Scheme Utilizing Adaptive Beam Search with Unmanned Aerial Vehicles in Smart Grid

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.

Examining the Capability of the VLF Technique for Nowcasting Solar Flares Based on Ground Measurements in Antarctica

Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using measurements from dayside propagation paths, and there remains limited focus on day–night mixed paths, which are important for method applicability. Between March and May of 2022, the Sun erupted a total of 56 M-class and 6 X-class solar flares, all of which were well captured by our VLF receiver in Antarctica. Using these VLF measurements, we reexamine the capability of the VLF technique to nowcast solar flares by including day–night mixed propagation paths and expanding the path coverage in longitude compared to that in previous studies. The amplitude and phase maximum changes are generally positively correlated with X-ray fluxes, whereas the time delay is negatively correlated. The curve-fitting parameters that we obtain for the X-ray fluxes and VLF signal maximum changes are consistent with those in previous studies for dayside paths, even though different instruments are used, supporting the flare-nowcasting method. Moreover, the present results show that, for day–night mixed paths, the amplitude and phase maximum changes also scale linearly with the logarithm of the flare X-ray fluxes, but the level of change is notably different from that for dayside paths. The coefficients used in the flare-nowcasting method need to be updated for mixed propagation paths.

Basis path coverage testing of MPI programs based on multi-task evolutionary optimization

A Message-Passing Interface (MPI) program usually consists of several processes, and a target path of this program is composed of a target sub-path selected in each process. However, there may be coverage conflict between different target sub-paths in the target path, and there is currently no effective approach to avoid this problem. Therefore, this paper proposes an approach of basis path coverage testing of MPI programs based on multi-task evolutionary optimization, which is used to generate a test case set that meets the criteria of branch and sentence coverage. Firstly, the proposed approach samples a certain number of representative test inputs, and calculates the coverage level of each test input for each target sub-path in the target path, which is used to group the target sub-paths with coverage conflict. Then, we construct an optimization model for each group of the target sub-paths, which can guide test case generation with high effectiveness. Finally, each optimization model is solved by an evolutionary algorithm, and the efficiency of solving these optimization models is improved based on a multi-task framework. The proposed approach is applied to basis path coverage testing of seven benchmark MPI programs under test, and compared with several state-of-the-art approaches. Experimental results indicate that the proposed approach has significant advantages in test case generation, as well as performs good scalability.

Automated Test Case Generation for Path Coverage by Using Multi-Objective Particle Swarm Optimization Algorithm with Reinforcement Learning and Relationship Matrix Strategies

Software testing is an important phase in software life cycle, and test case generation occupies the main part of the testing workload. Path coverage is the most stringent and difficult coverage criteria, so automatic test case generation for path coverage (ATCG-PC) is an important task in software testing. ATCG-PC is usually abstracted as an optimization search problem, and the commonly used solution algorithm is swarm intelligence algorithm. This paper proposes a path coverage test case generation algorithm based on improved multi-objective particle swarm optimization (IMOPSO) algorithm. The algorithm uses the multi-objective strategy to generate test cases for a group of similar paths at a time, and is supplemented by the particle action selection strategy based on reinforcement learning and the secondary search algorithm based on relationship matrix, which makes use of the similarity information between paths to speed up the early convergence and help to quickly reach the optimal solution around the local optimal position. By grouping the paths according to the similarity, the algorithm reduces the test case consumption, and improves the efficiency of test case generation. Experimental results show that, compared with the existing methods, the proposed algorithm can achieve higher path coverage with less test case consumption and is also suitable for large-scale programs.

Information-integration-based optimal coverage path planning of agricultural unmanned systems formations: From theory to practice

Industrial information integration engineering (IIIE) is an innovative research subject for analyzing complicated and large-scale systems. Autonomous and efficient path coverage of unmanned systems formations is an important subject of intelligent industrial agriculture. As one typical kind of complicated systems, agricultural unmanned systems formations are urgently required to optimize their operating trajectories. In this paper, an IIIE design for coverage path planning of the agricultural unmanned systems formations is presented as an IIIE application to verify the entire performances with considering the couplings between the formations and the working environment. In this design, one key concept of field-state iteration for information coupling integration is inherited and introduced in detail. Furthermore, its simulation models were developed based on structure (unmanned system agent structure and formation structure), geometry (map model and graph theory), dynamics (unmanned system agent model and formation model), and control (formation coverage path planning, formation control and trajectory recurrence) in the practice environments. The practice results were analyzed to validate the effectiveness of the proposed information integration design. Further, this paper puts forward a coverage path planning scheme for unmanned systems formations based on the rotating beam and improved probability roadmap algorithms, which can maintain 99.8% coverage rate, 0.08% repetition rate, and 0.007% redundant coverage rate while ensuring the optimal time. Then, two types of three-dimensional practice platform software including CarSim and Gazebo, are selected to graft the proposed algorithm into agricultural tractors formation and plant protection UAVs formation respectively, and the feasibility of the algorithm is verified under the condition closest to the real environment. Multiple experimentalresults demonstrate that the algorithm proposed in this paper has superior feasibility for engineering practice.

Multi-task collaborative method based on manifold optimization for automated test case generation based on path coverage

Automated test case generation based on path coverage is not only a large-scale black-box optimization problem, but also a key scientific problem in software automatic testing technology. Evolutionary algorithms and other search-based algorithms are representative methods for this problem. However, existing research mainly focuses on the multi-function case, where generating test cases for multiple functions is difficult due to the combinatorial explosion of the path number. In this paper, we propose a multi-task collaborative method based on manifold optimization by considering the topological manifold relationship between the test case space (decision space) and the program path space (target space). This method achieves the goal of collaborative optimization for different function coverage tasks by coordinating the allocation of computing resources and knowledge transfer mechanisms among them. To verify the effectiveness of the proposed method, we compare it with general solution methods for single-function automated test case generation based on path coverage, such as the manifold-inspired search-based algorithm. The experimental results show that the proposed method outperforms the compared single-function optimization algorithms especially on programs with strong coding similarities. This study verifies the feasibility of collaborative optimization methods for solving large-scale black-box optimization problems, such as the automated test case generation based on path coverage, and expands their application scenarios.

DCGFuzz: An Embedded Firmware Security Analysis Method with Dynamically Co-Directional Guidance Fuzzing

Microcontroller Units (MCUs) play a vital role in embedded devices due to their energy efficiency and scalability. The firmware in MCUs contains vulnerabilities that can lead to digital and physical harm. However, testing MCU firmware faces challenges due to various tool limitations and unavailable firmware details. To address this problem, research is turning to fuzzing and rehosting. Due to the inherent imbalance in computational resources of the fuzzing algorithm and the lack of consideration for the computational resource requirements of rehosting methods, some hardware behavior-related paths are difficult to discover. In this work, we propose a novel Dynamically Co-directional Guidance Fuzzing (DCGFuzz) method to improve security analysis efficiency. Our method dynamically correlates computational resource allocation in both fuzzing and rehosting, computing a unified power schedule score. Using the power schedule score, we adjust test frequencies for various paths, boosting testing efficiency and aiding in the detection of hardware-related paths. We evaluated our approach on nine real-world pieces of firmware. Compared to the previous approach, we achieved a maximum increase of 47.9% in path coverage and an enhancement of 27.6% in effective model coverage during the fuzzing process within 24 h.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted Models

Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted Models

Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Abstract Although accurately classifying signals from earthquakes and explosions at local distance (<250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.

Multi-task modeling and multifactorial optimization for path coverage problem of automated test case generation

Recent research in automated test case generation (ATCG) focuses on multi-objective optimization using functions based on path structure (F-PS) to solve the path coverage (PC) problem. Despite the similarity among F-PSs, the existing multi-objective optimization models fail to consider using the similarity to effectively promote optimization among multiple objectives. Inspired by the similarity and multitask optimization, this paper first establishes a multitasking path coverage (MtPC) model with two different F-PSs as its tasks. A multifactorial optimization framework for solving MtPC model (MfO-PC) is then proposed to optimize the tasks by assortative mating and to cooperatively generate desired test cases by automatic assignment strategy. Three multifactorial optimization algorithms based on the framework are then designed and tested on twelve benchmark programs. Experimental results show that the effectiveness of the proposed model and the designed algorithms based on MfO-PC framework achieve the highest path coverage with fewer test cases and less running time than some compared state-of-the-art algorithms.

Test data generation for covering mutation-based path using MGA for MPI program

Message Passing Interface (MPI) is a communication protocol used for parallel programming in various languages, valued for its reliability and broad applicability. Mutation testing is a software testing method for systematically simulating software faults. However, the significant number of inserted mutation branches in the program escalates the testing cost. To address this issue, we propose the comprehensive PMTDGM framework. The framework first generates mutation-based paths based on the relevancy of mutant branches and the difficulty of mutant branch coverage, followed by the establishment of a multitask model of path coverage. Finally, we employ a multi-population genetic algorithm (MGA) to generate test data. Our experiments, performed on six MPI programs of varying sizes and structures, demonstrate that the mutation-based paths have the small test sets and are easy to cover. Additionally, the multitask model and MGA can significantly improve the efficiency of generating test data and reduce the cost of mutation testing for MPI programs compared to traditional methods.

UAV search coverage under priority of important targets based on multi-location domain decomposition

<abstract> <p>In recent years, the coverage path planning (CPP) of unmanned aerial vehicles (UAVs) has attracted attention in reconnaissance, patrol, and search and rescue efforts, aiming to plan the paths for UAVs to cover a specified area as efficiently as possible. This paper proposes a UAV path fast coverage model to prioritize important targets with domain composition based on the starting point and location of the targets, combined with the domain decomposition strategy of important targets. Considering the constraints of the number of UAVs, the number of operators, and the flight time, the parallel search strategy can plan the coverage scheme with the shortest search time for the search range, and further obtain the coordinate points and path coordinates of the UAV turning. Finally, through multiple simulation experiments in four maps of various islands, the proposed method is verified to have an improved performance compare to the two track path coverage algorithms methods in terms of the coverage efficiency and the time complexity, thus providing a more scientific basis for the path coverage research of multi-target searches.</p> </abstract>

Rayleigh wave group velocities in North-West Iran: SOLA Backus-Gilbert vs. Fast Marching tomographic methods

In this study, we focus on Northwest Iran and exploit a dataset of Rayleigh-wave group-velocity measurements obtained from ambient noise cross-correlations and earthquakes.We build group-velocity maps using the recently developed SOLA Backus-Gilbert linear tomographic scheme as well as the more traditional Fast-marching Surface-wave Tomography method.The SOLA approach produces robust, unbiased local averages of group velocities with detailed information on their local resolution and uncertainty; however, it does not as yet allow ray-path updates in the inversion process. The Fast-marching method, on the other hand, does allow ray-path updates, although it does not provide information on the resolution and uncertainties of the resulting models (at least not without great computational cost) and may suffer from bias due to model regularisation.The core of this work consists in comparing these two tomographic methods, in particular how they perform in the case of strong vs. weak seismic-velocity contrasts and good vs. poor data coverage. We demonstrate that the only case in which the Fast-marching inversion outperforms the SOLA inversion is for strong anomaly contrasts in regions with good path coverage; in all other configurations, the SOLA inversion produces more coherent anomalies with fewer artefacts.

Proposed Test Case Generation Model using Fuzzy Logic (TCGMFL)

This research addresses the pressing need to enhance software testing, specifically focusing on white-box testing and basis path generation. Software testing is a linchpin in the software development process, ensuring software operates flawlessly and aligns with its intended objectives. However, this phase often incurs substantial time and resource investments. The primary aim of this study is to introduce an efficient and automated approach for basis path generation, a crucial component of white-box testing. The model commences by transforming source code into a tailored control flow graph (CFG), streamlining the automated generation of test paths. Central to this model is an algorithm for generating test paths (AGTP), meticulously traversing CFG nodes from source to destination. The algorithm’s design aims to comprehensively cover all test paths within the CFG. To enhance testing process efficiency, the model employs k-means clustering to generate and cluster inputs. Path coverage is rigorously assessed for each cluster, and fuzzy logic is used to determine the optimal path. The overarching goals of this research are to reduce time and financial costs associated with software testing while maintaining precision and efficiency. The model’s effectiveness in generating test cases is confirmed through the examination of multiple examples, underscoring its valuable contribution to software testing. This study marks a significant advancement toward more effective and cost-efficient software testing methodologies.

ESSENT: an arithmetic optimization algorithm with enhanced scatter search strategy for automated test case generation

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum path coverage with a minimized set of test cases. In ATCG-PC, the correlation among the dimensions of test cases is widely utilized in academia to minimize the search efforts of the search-based algorithm. Nevertheless, the information related to target path selection is not utilized, which leads to blind decision-making by the search-based algorithm during the target path selection. Therefore, this paper proposes an enhanced scatter search strategy by using opposition-based learning. An arithmetic optimization algorithm is also proposed to solve ATCG-PC based on the enhanced scatter search strategy, namely, ESSENT. The ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path, and generates new test cases that cover the target path by modifying the dimensions of the existing test cases. The performance of the ESSENT algorithm is evaluated on six iFogSim subprograms and six Stanford coreNLP subprograms. Experiment results show that the ESSENT algorithm achieves a higher convergence rate than other state-of-the-art algorithms. Furthermore, it enables maximum path coverage with fewer test cases.

Improved Path Testing Using Multi-Verse Optimization Algorithm and the Integration of Test Path Distance

Emerging technologies in artificial intelligence (AI) and advanced optimization methodologies have opened up a new frontier in the field of software engineering. Among these methodologies, optimization algorithms such as the multi-verse optimizer (MVO) provide a compelling and structured technique for identifying software vulnerabilities, thereby enhancing software robustness and reliability. This research investigates the feasibility and efficacy of applying an augmented version of this technique, known as the test path distance multiverse optimization (TPDMVO) algorithm, for comprehensive path coverage testing, which is an indispensable aspect of software validation. The algorithm’s versatility and robustness are examined through its application to a wide range of case studies with varying degrees of complexity. These case studies include rudimentary functions like maximum and middle value extraction, as well as more sophisticated data structures such as binary search trees and AVL trees. A notable innovation in this research is the introduction of a customized fitness function, carefully designed to guide TPDMVO towards traversing all possible execution paths in a program, thereby ensuring comprehensive coverage. To further enhance the comprehensiveness of the test, a metric called ‘test path distance’ (TPD) is utilized. This metric is designed to guide TPDMVO towards paths that have not been explored before. The findings confirm the superior performance of the TPDMVO algorithm, which achieves complete path coverage in all test scenarios. This demonstrates its robustness and adaptability in handling different program complexities.