Traditional Testing Techniques Research Articles

Cardiovascular responses to heat and cold exposure are altered by preterm birth in guinea pigs.

Adversity early in life can modify the trajectory for disease risk extending decades beyond the event. Preterm birth produces persistent cardiovascular alterations that may appear maladaptive in adulthood. We have previously hypothesized that those born preterm may exhibit cardiovascular vulnerability in the climate change context. Further, this vulnerability may be present as early as childhood. We aimed to identify the early signs of cardiovascular dysfunction at childhood-equivalent age using our animal model of preterm birth. Using a whole-body thermal stress test, guinea pigs aged 35-d and 38-d (equivalent to 8-10-year-old children) and born at term or preterm gestations were exposed to progressive hyper- (TC = 41.5°C) and hypo-thermia (TC = 34°C; normothermia TC = 39°C). Comprehensive cardiovascular monitoring included ECG, blood pressure, microvascular perfusion, blood gas, and catecholamine profile, as well as skin and core body temperature. Preterm-born animals exhibited attenuated vascular responses to hyperthermic stress, and a significant elevation in systolic blood pressure in response to hypothermic stress. Such responses are similar to those observed in elderly populations and indicate the presence of cardiovascular dysfunction. This is the first study to demonstrate the impact of preterm birth on the cardiovascular response to both heat and cold stress. Further, this dysfunction has been observed at an earlier age than that achievable using traditional stress testing techniques. The present findings warrant further investigation.

Program Segment Testing for Human–Machine Pair Programming

Human–Machine Pair Programming (HMPP) is a promising technique in the software development process, which means that software construction can be done in the manner that humans are responsible for developing the program while computer is responsible for monitoring the program in real-time and reporting errors. The Java runtime exceptions in the current version of the software under construction can only be effectively detected by means of its execution. Traditional software testing techniques are suitable for testing completed programs but face a challenge in building a suitable testing environment for testing the partial programs produced during HMPP. In this paper, we put forward a novel technique, called Program Segment Testing (PST) for automatically identifying errors caused by runtime exceptions to support HMPP. We first introduce the relevant involved in this technique to detect index out of bounds exceptions, a representative of runtime exceptions. Then we discuss the methodology of this technique in detail and illustrate its workflow with a simple case study. Finally, we carry out an experiment to evaluate this technique and compare it with three existing fault detection techniques using several programs to demonstrate its effectiveness.

Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics

Abstract With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.

Experimental verification and rapid estimation of uncalibrated cable force via video-based and vibration-based measurements.

The stayed-cable is an important component of cable-stayed bridges, with cable force being a focal point during construction and bridge operation. The advancement of camera and image processing technology has facilitated the integration of computer vision technology in structural inspection and monitoring. This paper focuses on enhancing cable force measurement methods and addressing the limitations of traditional testing techniques by conducting experimental research on cable force estimation using video recording. The proposed approach involves capturing video footage of the target on the cable with a smartphone. Subsequently, a combination of techniques such as the background subtraction method, image morphology processing, and Hough transform image processing technology are employed to detect the precise center coordinates and ultimately obtain the accurate displacement-time curve of the cable's vibration. In addition, the graphic Circularity Coefficient (CC) has been introduced to assess its effectiveness in post-motion-blur image processing for circular targets. The fundamental frequency of the cable is determined by the fast Fourier transformation, and the relationship between the cable force and the fundamental frequency is used to estimate the cable force. The experimental results are compared with data from accelerometers and force gauges, demonstrating that the frequency measurement error is below 1.2% and the cable force test error is less than 3%. In the process of acquiring the cable's fundamental frequency, the test directly employs the pixel as the displacement unit, eliminating the need for image calibration. The innovative use of the CC in processing motion-blurred targets ensured accurate recognition of target coordinates. The experimental findings highlight the method's simplicity, speed, and accuracy.

Experimental and Machine learning investigation of Potential strength of recycled aggregate concrete

Recycled Aggregate Concrete (RAC) offers a promising perspective for sustainable construction through the use of recycled materials to minimize environmental damage. This work uses a dual approach that integrates experimental analysis and machine learning to comprehensively explore the potential effectiveness of RAC. The experimental research process involves the formulation of concrete mixes using recycled aggregate followed by comprehensive testing to assess its compressive strength, flexural strength and durability. A machine learning model is developed to predict concrete strength using data-driven techniques to improve understanding. The study is to identify and evaluate the viability of using RAC as a long-term and environmentally friendly building material through the integration of traditional testing techniques and contemporary predictive analysis. (Citation: Chandra, S., Berntsson, L., 2002; Tam, V.W.Y. et al., 2007; Silva, R.V. et al., 2014).

Towards an in-process ultrasonic phased array inspection method for narrow-gap welds

The marriage of welding and Non-Destructive Testing (NDT) processes at the point of manufacture has enabled the detection and correction of defects during the welding process. This has demonstrated clear financial and production benefits by reducing weld rework and ensuring schedule certainty, however this is yet to be demonstrated for use with narrow-groove welding practises. Narrow-groove welds are notoriously difficult to inspect using traditional Phased Array Ultrasonic Testing (PAUT) techniques due to large thicknesses and the vertical nature of Lack-of-Sidewall Fusion (LOSWF) defects. This is further complicated by the presence of partially-filled weld geometries during in-process inspection, which cause geometric reflections which can mask or falsely indicate the presence of a defect. A solution to this is proposed in this work, by adapting a dual-tandem phased array imaging system for the imaging of LOSWF defects in a partial weld geometry. This considers a two array system utilising a phased array probe on each weld side, coupled with an advanced dual-aperture Full Matrix Capture (FMC) acquisition technique. Advanced multi-mode image processing algorithms such as the Total Focusing Method (TFM) and Phase Coherence Imaging (PCI), with adaptive delay law calculation, have shown high sensitivity to LOSWF defects in a mock partial weld geometry. Additionally, an adaptive Probe Centre Spacing (PCS) technique is defined for in-process inspection based on amplitude and phase coherence sensitivity in partial weld geometries, with the effects of partial weld reflections analysed and discussed. These results have demonstrated the effectiveness of a dual-tandem phased array approach to imagine LOSWF defects during the in-process inspection of narrow-gap welds.

Enhancing Software Quality through Early-Phase of Software Verification and Validation Techniques

Purpose: The core objectives of this conducted research were threefold: first, to assess whether early-phase verification and validation (V&V)techniques, encompassing software design review, code review, and inspection, significantly contribute to defect prevention throughout the software development process; second, to evaluate the impact of these techniques on the efficiency of software development in terms of time, cost, and resource allocation; and third, to conduct a comprehensive comparative analysis between early-phase verification and validation (V&V)techniques and traditional post-development testing regarding their effectiveness in defect prevention and software quality improvement.
 Methodology: Employing an experimental approach, this study conducted case studies within Tanzanian software development organizations. Data were meticulously gathered through surveys and interviews involving software professionals. Both quantitative and qualitative data were systematically analyzed to ensure data reliability and ethical considerations.
 Findings: The research findings indicate that early-phase verification and validation (V&V) techniques shine as powerful tools for defect prevention, leading to a substantial reduction in defect counts and elevating software quality metrics, such as reliability and user satisfaction. Notably, these techniques demonstrated a favorable impact on software development efficiency by shortening development cycles and curtailing costs. When compared to traditional post-development testing, early-phase verification and validation (V&V) techniques consistently outperformed in the realm of defect prevention and software quality enhancement.
 Unique Contribution to Theory, Practice and Policy: The study's outcomes underscore the transformative potential of early-phase verification &validation techniques, urging the software development industry to pivot from defect detection to defect prevention. The insights garnered here offer practical guidance for organizations aiming to streamline their software development processes, economize costs, and deliver top-notch software products that closely align with user expectations. While conducted in a Tanzanian context, these findings reverberate across the global software development landscape, underscoring the universal relevance of early-phase verification and validation (V&V) techniques as pivotal enablers of software quality assurance and operational efficiency.

Expert consensus on clinical localization detection standards for metagenomic next generation sequencing of pathogens

Infectious diseases seriously endanger human health. Rapid and accurate detection of pathogens is the key to effective treatment and precise prevention and control of infectious diseases. Traditional pathogen testing techniques cover a limited variety of microorganisms, making it difficult to meet clinical needs. Metagenomic Next Generation Sequencing (mNGS) theoretically can simultaneously detect all known gene sequences of pathogens, greatly improving the clinical capacity of diagnosing and treating patients with severe, complicated, rare, and emerging pathogen infections. In the context of individualized health needs, the self-built testing of new technologies in laboratories can continuously improve the level of clinical diagnosis and treatment. Due to the high starting point of research and development and the complex operation process of the mNGS technology platform, this study reaches a consensus on the experimental procedure, assay validation, quality control, and report review of the mNGS-LDT in the fields of clinical testing, infection, critical care, and in vitro diagnosis to ensure the appropriate application of the technology and guarantee patient safety and proposes standardized requirements and suggestions.

An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

The Graphical User Interface (GUI) is the most promising factor in the Software Development Lifecycle (SDL), which allows the users to interact with the system. To ensure user-friendliness, GUI Testing (GT) is required. The traditional testing techniques attained flawed results due to having inappropriate functions. Hence, Global Decaying Probabilistic Long Short-Term Memory (GDP-LSTM) and Standard Deviation Q-Learning (SDQL)-based automatic testing for GUI are proposed as solutions. Initially, the Test Case (TC) and GUI are extracted from the historical data and are subjected to Region of Interest (ROI) analysis. Here, an appropriate ROI is analyzed by Module Coupling Slice (MCS), and it is fed into Hadoop Parallelization (HP). Now, Spectral Kernelized Gaussian Clustering (SKGC) and Non-Linear Elite Guided Optimized Ant Colony (NE-GO-AC) are used to perform mapping and reducing, respectively. Likewise, the parallelized output is utilized to construct the Document Object Model (DOM) tree. Then, the attributes are extracted and given to the GDP-LSTM classifier that effectively predicts whether GUIs are desirable or undesirable. Then, the undesirable results are inputted into a SDQL-based deviation analysis. If the deviation is low, it is assumed as an update; otherwise, it is considered as an error. The experimental analysis depicted that the proposed system attained high dominance with 98.89% accuracy in the prevailing models.

The Current Application of Traditional Forensic Psychological Testing Techniques—Analysis of the Application of Psychological Testing Technology in Basic Procuratorial Institutions

The Current Application of Traditional Forensic Psychological Testing Techniques—Analysis of the Application of Psychological Testing Technology in Basic Procuratorial Institutions

An Interleaving Guided Metamorphic Testing Approach for Concurrent Programs

Concurrent programs are normally composed of multiple concurrent threads sharing memory space. These threads are often interleaved, which may lead to some non-determinism in execution results, even for the same program input. This poses huge challenges to the testing of concurrent programs, especially on the test result verification, that is, the prevalent existence of the oracle problem. In this paper, we investigate the application of metamorphic testing, a mainstream technique to address the oracle problem, into the testing of concurrent programs. Based on the unique features of interleaved executions in concurrent programming, we propose an extended notion of metamorphic relations, the core part of metamorphic testing, which are particularly designed for the testing of concurrent programs. A comprehensive testing approach, namely ConMT, is thus developed and a tool is built to automate its implementation on concurrent programs written in Java. Empirical studies have been conducted to evaluate the performance of ConMT, and the experimental results show that in addition to addressing the oracle problem, ConMT outperforms the baseline traditional testing techniques with respect to higher degree of automation, better bug-detection capability, and shorter testing time. It is clear that ConMT can significantly improve the cost-effectiveness for the testing of concurrent programs and thus advances the state of the art in the field. The study also brings novelty into metamorphic testing, hence promoting the fundamental research of software testing.

Evaluation of material surface properties using one-way collinear mixing Rayleigh waves

Nonlinear Rayleigh waves are useful for material surface damage detection. Traditional non-destructive testing (NDT) techniques based on higher harmonics of Rayleigh waves are inevitably interfered with by the nonlinearity of measurement systems. To overcome this disadvantage, an approach based on one-way collinear mixing Rayleigh waves (MRWs) is proposed to evaluate the material surface properties. A wedge with two different incidence angles for the excitations of transverse and longitudinal waves is designed to generate one-way collinear MRWs. The one-way collinear MRWs can be generated on the whole surface of the material, and propagate along the propagation way under one excitation. Numerical simulations of one-way collinear MRWs under different surface properties, which are characterized using different third-order elastic constants, are studied on a semi-infinite aluminum. Experiments are also conducted on the aluminum specimen, whose surface is corroded by hydrochloric acid so as to change the material surface properties. Both numerical and experimental results show that the one-way collinear MRWs are generated effectively, and the defined relative acoustic nonlinearity parameter increases with the propagation distance as well as the severity of the material surface damages. The results verify the effectiveness of the proposed MRW-based method and prove that it would be a promising NDT technique for the accurate evaluation of material surface properties.

Research on the surface crack detection of specimen manufactured by selective laser melting based on local singular value decomposition method

SLM (selective laser melting) is an important additive manufacturing technology. Parts with complex structures can be produced by SLM, so surface cracks and other defects within SLM parts are difficult to be identified by traditional nondestructive testing techniques. In this paper, laser ultrasonic technology is proposed to identify the surface crack of SLM specimen. Because of the anisotropy and residual stress of the specimen, the time signal is strongly interfered with by noise, which makes the identification and location of the crack difficult. To solve this problem, the phase space was reconstructed from the time signal and analyzed by the local singular value decomposition method. According to the result of the experiment, this method can reveal the variation of the phase space and has a good effect on the identification and location of the surface crack whose depth is larger than 1 mm.

Reexamination of traditional testing techniques for determining WRCs of woven geotextiles in full suction range

Woven geotextiles have been widely used in soil infrastructures for the reinforcement purpose. The hydraulic properties of a woven geotextile are not major reinforcement design parameters and the water retention capability of a woven geotextile is often ignored. The traditional testing techniques were designed for soils or nonwoven geotextiles, but not for woven geotextiles. Nowadays, a new type of woven geotextile with wicking fibers was developed which could be used for both drainage and reinforcement purposes. However, there are no proper testing techniques to determine the full-range water retention curve (WRC) for a woven geotextile, let alone for the wicking geotextile.This paper aimed at proposing a proper testing technique to determining the full-range WRC for the wicking geotextile and to compare the water retention capability of wicking and non-wicking geotextiles. Firstly, the traditional testing techniques were re-examined to check the suitability for characterizing the WRCs of woven geotextiles whose pore size distributions were anisotropic. Secondly, a proper testing technique was proposed and the WRCs of different types of woven geotextiles were determined. Thirdly, the WRCs of wicking and non-wicking geotextiles were compared to demonstrate the advantages of the wicking geotextile to hold and transport water under unsaturated conditions. Finally, the effect of wicking fiber on the water retention capability of the wicking geotextile was quantified.

Model‐checking‐driven explorative testing of CRDT designs and implementations

AbstractInternet‐scale distributed systems often replicate data at multiple geographic locations to provide low latency and high availability, despite node and network failures. According to the CAP theorem, low latency and high availability can only be achieved at the cost of accepting weak consistency. The conflict‐free replicated data type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas. CRDTs have been notoriously difficult to design and implement correctly. Subtle deep bugs lie in the complex and tedious handling of all possible cases of conflicting data updates. We argue that the CRDT design should be formally specified and model checked, to uncover deep bugs which are beyond human reasoning. The implementation further needs to be systematically tested. On the one hand, the testing needs to inherit the exhaustive nature of the model checking and ensures the coverage of testing. On the other hand, the testing is expected to find coding errors which cannot be detected by design level verification. Toward the challenges above, we propose the model‐checking‐driven explorative testing (MET) framework. At the design level, MET uses TLA+ to specify and model check CRDT designs. At the implementation level, MET conducts model‐checking‐driven explorative testing, in the sense that the test cases are automatically generated from the model‐checking traces. The system execution is controlled to proceed deterministically, following the model‐checking trace. The explorative testing systematically controls and permutes all nondeterministic choices of message reorderings. We apply MET in our practical development of CRDTs. The bugs in both designs and implementations of CRDTs are found. As for bugs which can be found by traditional testing techniques, MET greatly reduces the cost of fixing the bugs. Moreover, MET can find subtle deep bugs which cannot be found by existing techniques at a reasonable cost. Based on our practical use of MET, we discuss how MET provides us with sufficient confidence in the correctness of our CRDT designs and implementations.Conflict‐free replicated data type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas in distributed systems. CRDTs have been notoriously difficult to design and implement correctly. We propose model‐checking‐driven explorative testing (MET) framework for dealing with such problem. We apply MET in our practical development of CRDTs. MET successfully finds subtle deep bugs and provides us with sufficient confidence in the correctness of our CRDT designs and implementations.

Dual-tandem phased array method for imaging of near-vertical defects in narrow-gap welds

Near-vertical flaws in thick-section narrow-gap welds are notoriously difficult to detect and size using traditional Phased Array Ultrasonic Testing (PAUT) techniques. Self-tandem shear inspection has been considered for detection but lacks appropriate full weld sensitivity. The dual-tandem phased array inspection method is proposed to enable multi-mode Total Focusing Method (TFM) pulse-echo imaging with longitudinal through-transmission inspection, by introducing a second, opposite-facing probe on the far weld side. Pulse-echo and through-transmission datasets are obtained in a single Full Matrix Capture (FMC) acquisition, for each weld side. Analysis of the optimum Probe Centre Spacing (PCS) based upon image sensitivity suggests that a spacing corresponding to a longitudinal beam crossover at 2/3 sample thickness provides greatest weld coverage. Testing on near-vertical EDM notches in a 120 mm thick mock narrow-gap carbon steel sample has shown high sensitivity TFM imaging, at notch depths of 27.5 mm, 60 mm and 92.5 mm. Pitch-catch through-weld transmission imaging has exhibited tip-diffraction images with a Signal-to-Noise Ratio (SNR) of up to 17.3 dB. Pulse-echo imaging using multi-mode TFM has shown tip-diffraction and reflection with up to 19.2 dB and 34.8 dB SNR respectively. The possibility of image mixing by considering image defect response type has provided full 2D notch reconstruction. These results demonstrate the strength of the dual-tandem method in enhancing the detection reliability and detection of near-vertical defects in thick-section narrow-gap welds.

AI and Blockchain-based source code vulnerability detection and prevention system for multiparty software development

With the growing demand for application software, there is a race among industries to develop software as quickly as possible. However, maintaining pace and ensuring bug-free software has become increasingly challenging in a work-from-home arrangement as software developers are not under constant supervision. It increases the possibility of buggy products, and traditional testing techniques fail to provide optimal performance. We propose an Artificial Intelligence (AI) and blockchain-based novel decentralized software testing system. The proposed system aims to detect and prevent vulnerable code by synergizing deep learning capabilities and smart-contract-powered blockchain. The vulnerability detection is performed automatically without relying on manually written rules. We propose a non-vulnerability score range map to classify the source code. Furthermore, we integrate an InterPlanetary File System (IPFS) to ensure efficient storage over the blockchain. We conduct a testbed-based experiment to demonstrate the effectiveness of AI and blockchain integration for secure code development and testing.

Review on monitoring of transformer insulation oil using optical fiber sensors

Power transformers are critical components of power system networks. Failure of this equipment risks the entire system, resulting in power outages and income losses. The reliability of this device is generally determined by its insulating quality. Hence, it is essential to check the transformer's insulation oil condition regularly. Traditional test techniques for assessing the condition of power transformers insulation oil have several drawbacks. Optical fiber-based sensors for monitoring transformer oil conditions have been designed to address such issues. This paper presents a brief review of optical fiber sensors for measuring oil level, oil aging assessment, moisture content and temperature in transformer oil. This review shall be of benefit to both engineers and researchers who are doing research in this field.

An Improved Structural Health Monitoring Method Utilizing Sparse Representation for Acoustic Emission Signals in Rails

The structural health of rails significantly impacts the safety of railway transport, which requires long-term and accurate monitoring. An improved structural health monitoring (SHM) method is proposed to enhance the damage information and construct an adaptive weighted index for evaluating the structural health states of rails. In this method, modified singular value decomposition (SVD) with a double-layer Hankel matrix compensation algorithm enhances the damage information and ensures real-time detection. Based on the inflection point of the inner product gradient, the step size of the sparsity adaptive matching pursuit (SAMP) algorithm is adaptively adjusted to reconstruct damage signals. Subsequently, an innovative index is constructed with adaptive weighting for SHM of rails. Tensile tests are performed to verify the effectiveness of the proposed method. The results show that the proposed method avoids the defects of traditional nondestructive testing (NDT) techniques that rely on a priori information about the damage signal (such as damage frequency acoustic emission (AE) hits rate and waveform characteristics). Furthermore, using the variation in signal energy, the proposed method has the capability to accurately delineate the three structural health stages under complex waveform conditions, which is not possible with conventional detection methods. This method provides guidance for the damage analysis and monitoring techniques of rails.

A Framework for Predicting Airfare Prices Using Machine Learning

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K-nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion. Index Terms— Machine learning, Prediction model, Airline price prediction, Software testing,