Test Time Reduction Research Articles

Using Soluble CD38 to Overcome Daratumumab Interference in Pretransfusion Compatibility Testing

Background: CD38 is a protein highly expressed on myeloma (MM) cells that has been shown to be an effective target antigen for monoclonal antibody therapies, so treatment with anti-CD38 monoclonal antibodies is a first line therapy for patients with MM. Although CD38 is weaker expressed on erythocytes anti-CD38 binds to CD38 on reagent RBCs and cause panreactivity in vitro and subsequent false positive reactions in indirect antiglobulin tests (IAT), antibody detection (screening) tests, antibody identification panels, and anti-human globulin (AHG) crossmatches. These findings suggest that the soluble CD38 method could improve transfusion safety for patients receiving anti-CD38 therapy, particularly in urgent clinical settings This study aims to evaluate the effectiveness of a new soluble CD38-based method in mitigating Daratumumab interference during pretransfusion compatibility testing." Methods: We evaluated the Grifols sCD38 method in 20 patients and compared it with the DTT method Results: In our experience the sCD38 method reduced testing time from 150 to 50 minutes and demonstrated 100% efficacy, compared to 90% with DTT. The sCD38 effectively mitigated the interference caused by anti-CD38 antibodies in 10 (100% efficacy) of patient samples tested while DTT was successful in only 9/10 (90% efficacy); no interference was observed in patients presenting anti erythrocyte antibodies. Moreover, there was no negative influence on DTT sensitive blood group systems such as KEL upon sCD38 treatment. Conclusions: In our Laboratory, in the year 2023, we processed 129 patients treated with anti-CD38. Therefore the reduction from 150 to 50 minutes in the time needed to perform tests for mitigation of anti-CD38 interference appears to be relevant with a recovery of approximately 210 technical-hours per year. Another highly appreciated operational aspect was the possibility of treating the patient's plasma and perform tests using automatic instruments (Erytra Grifols) available in our laboratory. In the evaluation of this new method, we did not observe failures in the mitigation of anti-CD38 interference. Furthermore, the results obtained in the samples that presented allo or auto antibodies were not affected by the treatment with sCD38. In our experience Grifols sCD38 assay is straightforward and quick to perform ant it is superior to DTT treatment in the mitigation of anti-CD38 antibody interference in MM patients treated with Daratumumab.

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

Duration versus accuracy—what matters for computerised adaptive testing in schools?

AbstractBackgroundComputerised adaptive tests (CATs) are tests that provide personalised, efficient and accurate measurement while reducing testing time, depending on the desired level of precision. Schools have different types of assessments that can benefit from a significant reduction in testing time to varying degrees, depending on the area of application, but for which the loss of measurement accuracy has a different impact. The implementation of CAT can take several approaches, each of which can potentially affect the resulting test length and accuracy.ObjectivesWe compare the methods of estimation‐based CAT and binary‐search‐based CAT to determine the extent to which they are suitable for school assessment in terms of their length and accuracy.MethodsThis study uses simulations based on empirical data from a cohort of pupils with and without special needs (n = 400) to examine the effects of probabilistic estimation‐based CAT and deterministic binary‐search‐based CAT on the length and accuracy of an adaptive reading test for pupils with different ability levels.Results and ConclusionsEstimation‐based CAT leads to a 40% test reduction with an average accuracy of r = 0.96, while binary‐search‐based CAT leads to a test reduction of up to 88% with an average accuracy of r = 0.83. Both methods demonstrate the applicability of CAT in educational environments. Practical advantages and disadvantages of both methods for learning environments are discussed, as well as which method is best suited for specific assessment needs.

A-052 Clinical usefulness of ADAMS A1c HA-8190V automatic switching mode for the HbA1c measurement in Hb variants

Abstract Background High-performance liquid chromatography (HPLC) is widely used as a HbA1c routine assay. However, quantification of glycated hemoglobin (HbA1c) is a challenge in patients with hemoglobin (Hb) variants. In this study, we evaluated the clinical utility and analytical performance of new developed ARKRAY ADMAS HA-8190V assay using autoswitch mode, that automatically switches between variant and fast mode depending on patient Hb characteristics, in patients with Hb variant suspected in Korea. Methods The measuring time for HbA1c assay was compared between the HA-8180V fast mode (existing model) and the autoswitch mode of HA-8190V (new model) in 60 normal Hb samples. And the HbA1c measured values was compared bewteen the HA-8180V fast mode and the HA-8190V autoswitch mode in 30 low Hb samples. In addition, flag detection for the abnormal Hb and measured values for 21 Hb variant samples were analyzed using the HA-8180V fast mode and the HA-8190V autoswitch mode. Results It took 50.8 minutes and 39.1 minute to measure 60 normal Hb samples by HA-8180V fast mode and HA-8190V autoswitching mode, respectively. The values could not be measured at the HA-8180V fast mode for 7 of 30 low Hb samples, whereas all samples could be measured at HA-8190V autoswitching mode. For 21 Hb variant samples, HA-8180V fast mode showed abnormal flag detections for only 14 samples, but HA-8190V autoswitch mode reported abnormal variant flags for 20 samples. In the result accuracy evaluation (% difference) based on capillary electrophoresis HbA1c assay (Sebia, Lisses, France) in the 21 Hb variant samples, the % difference of HA-8190 V autoswitch mode was 3.9%, which was superior to HA-8180V fast mode of -11.65%. Conclusions Autoswitch mode of The HA-8190V (automatically switch between variant and fast mode based on patient Hb characteristics) showed more accurate flag detection and HbA1c measurements in patients with Hb variants, along with shorter test time and manual reduction.

Unsteady conjugate heat transfer simulation around the throat insert in a high enthalpy shock tunnel

High enthalpy shock tunnel is a critical ground testing facility for evaluating the aerodynamic performance of hypersonic vehicles by simulating high-enthalpy, hypersonic flows. However, as the total temperature or pressure of the stagnant gas rises, the throat insert may be subjected to melting or oxidation, leading to a degradation in flow quality or a reduction in effective test time. This study investigates the unsteady heat transfer between the gas flow and the throat insert using a conjugate heat transfer model. The effects of test time, total temperature, and total pressure on the throat temperature are presented. An increase in any of these factors will raise the throat temperature. Moreover, oxidation will cause the copper throat insert to melt more quickly under the same conditions, which should be considered when addressing throat melting issues.

Multiprobe Planar Near-field Range Antenna Measurement System with Improved Performance

This article presents a novel multiprobe planar near-field range (PNFR) measurement system. The said system simplifies the overall mechanical design, making it simpler than the existing scanning probe PNFR measurements, and also significantly reduces testing time. A dielectric-based probe is introduced to reduce the antenna size, thereby improving resolution. The probe under consideration is an antipodal Vivaldi antenna offering broadband support and ensuring wideband characteristics of aerials. Numerical results for representative X-band antenna models, presented in the Matlab environment, demonstrate robust performance of the developed measurement system.

Impact of Critical Success Factors on Productivity Gain during Automation Testing

Software testing, a linchpin of quality assurance in software development, encompasses both manual and automation approaches. While manual testing ensures meticulous scrutiny, automation accelerates efficiency, reducing time and costs in dynamic landscape of software development. This research investigates the impact of critical success factors on productivity gain in software testing, with a focus on automation, within CMMI Level 5 companies. Drawing from a dataset comprising thirty real-time projects spanning banking, retail, and industrial applications, study explores dynamics of software development under agile and waterfall models. Interviews with developers and testers reveal insights into key aspects influencing productivity, including skills, experiences, and training. Emphasizing the need for strategic automation in projects subject to changes and maintenance, the research analyzes over thirty projects, scrutinizing variables such as manually designed test cases, execution time, and subsequent automation of test cases. Strategic selection of test cases for automation emerges as a cost-efficient practice. Results highlight a correlation between manual and automated test cases, indicating productivity gains. Research introduces a productivity gain metric, showcasing a break-even point where automation significantly reduces testing time. Overall, the findings offer a comprehensive understanding of software testing within CMMI Level 5, guiding organizations toward efficient automation practices and improved productivity.

The irtQ R package: a user-friendly tool for item response theorybased test data analysis and calibration.

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package (http://CRAN.R-project.org/), which simplifies IRTbased analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Audiogram Estimation Performance Using Auditory Evoked Potentials and Gaussian Processes.

Auditory evoked potentials (AEPs) play an important role in evaluating hearing in infants and others who are unable to participate reliably in behavioral testing. Discriminating the AEP from the much larger background activity, however, can be challenging and time-consuming, especially when several AEP measurements are needed, as is the case for audiogram estimation. This task is usually entrusted to clinicians, who visually inspect the AEP waveforms to determine if a response is present or absent. The drawback is that this introduces a subjective element to the test, compromising quality control of the examination. Various objective methods have therefore been developed to aid clinicians with response detection. In recent work, the authors introduced Gaussian processes (GPs) with active learning for hearing threshold estimation using auditory brainstem responses (ABRs). The GP is attractive for this task, as it can exploit the correlation structure underlying AEP waveforms across different stimulus levels and frequencies, which is often overlooked by conventional detection methods. GPs with active learning previously proved effective for ABR hearing threshold estimation in simulations, but have not yet been evaluated for audiogram estimation in subject data. The present work evaluates GPs with active learning for ABR audiogram estimation in a sample of normal-hearing and hearing-impaired adults. This involves introducing an additional dimension to the GP (i.e., stimulus frequency) along with real-time implementations and active learning rules for automated stimulus selection. The GP's accuracy was evaluated using the "hearing threshold estimation error," defined as the difference between the GP-estimated hearing threshold and the behavioral hearing threshold to the same stimuli. Test time was evaluated using the number of preprocessed and artifact-free epochs (i.e., the sample size) required for locating hearing threshold at each frequency. Comparisons were drawn with visual inspection by examiners who followed strict guidelines provided by the British Society of Audiology. Twenty-two normal hearing and nine hearing-impaired adults were tested (one ear per subject). For each subject, the audiogram was estimated three times: once using the GP approach, once using visual inspection by examiners, and once using a standard behavioral hearing test. The GP's median estimation error was approximately 0 dB hearing level (dB HL), demonstrating an unbiased test performance relative to the behavioral hearing thresholds. The GP additionally reduced test time by approximately 50% relative to the examiners. The hearing thresholds estimated by the examiners were 5 to 15 dB HL higher than the behavioral thresholds, which was consistent with the literature. Further testing is still needed to determine the extent to which these results generalize to the clinic. GPs with active learning enable automatic, real-time ABR audiogram estimation with relatively low test time and high accuracy. The GP could be used to automate ABR audiogram estimation or to guide clinicians with this task, who may choose to override the GP's decisions if deemed necessary. Results suggest that GPs hold potential for next-generation ABR hearing threshold and audiogram-seeking devices.

Use of high-resolution fluorescence in situ hybridization for fast and robust detection of SARS-CoV-2 RNAs

Early, rapid, and accurate diagnostic tests play critical roles not only in the identification/management of individuals infected by SARS-CoV-2, but also in fast and effective public health surveillance, containment, and response. Our aim has been to develop a fast and robust fluorescence in situ hybridization (FISH) detection method for detecting SARS-CoV-2 RNAs by using an HEK 293 T cell culture model. At various times after being transfected with SARS-CoV-2 E and N plasmids, HEK 293 T cells were fixed and then hybridized with ATTO-labeled short DNA probes (about 20 nt). At 4 h, 12 h, and 24 h after transfection, SARS-CoV-2 E and N mRNAs were clearly revealed as solid granular staining inside HEK 293 T cells at all time points. Hybridization time was also reduced to 1 h for faster detection, and the test was completed within 3 h with excellent results. In addition, we have successfully detected 3 mRNAs (E mRNA, N mRNA, and ORF1a (−) RNA) simultaneously inside the buccal cells of COVID-19 patients. Our high-resolution RNA FISH might significantly increase the accuracy and efficiency of SARS-CoV-2 detection, while significantly reducing test time. The method can be conducted on smears containing cells (e.g., from nasopharyngeal, oropharyngeal, or buccal swabs) or smears without cells (e.g., from sputum, saliva, or drinking water/wastewater) for detecting various types of RNA viruses and even DNA viruses at different timepoints of infection.

Force feedback reduces test time and interaction forces in telemanipulated palpation using a robotic endoscope with series elastic actuated joints.

Abstract Force feedback is currently missing in most surgical robot systems, even though it would be essential for many surgical tasks, such as palpating tissue. In this study, we used a previously developed robotic endoscope based on series elastic actuation and tested the feasibility of using it for force feedback with a pilot study. The participants had a lower test time (−27%) and lower applied forces (−23%) with force feedback enabled. While further studies are needed for validation in realistic surgical scenarios, these results suggest improved safety and efficiency in telemanipulated surgeries.

Need of voltage and capacity profile reconstruction in incremental capacity analysis for second life batteries from repurposer perspective

Incremental Capacity Analysis (ICA) is a widely used non-destructive technique to predict State of Health (SoH) of Lithium-ion Battery (LiB). Till today, the SoH is judged on ICA magnitude of peaks and valleys for defining the health indicators. This research work provides the state-of-the art in voltage and capacity profile reconstruction available for ICA through Constant Capacity (CC), Constant Voltage (CV), and hybrid sampling. Here, hybrid sampling offers flexibility for the repurposer to define voltage bands for partial charge and discharge limits according to ICA peaks. This research work aims to address the scope of reducing time and efforts on partial charging and discharging tests conducted by the repurposers for SoH and Remaining useful Life (RuL) estimation of Second Life Battery (SLB). Nissan Leaf battery 2011 model taken from EV application is tested for health indicators catering insights on SoH and RuL by reconstructing the voltage and capacity profile. The necessity of a filter can be neglected as a part of ICA post-processing. The peaks and valleys from reconstructed profile is aiding the repurposer to define voltage limits for partial charging and discharging based on SoH. This led to reduction in testing time and efforts in the range of 16% to 83% during SLB characterization. The accuracy of this technique is validated by varying the sampling frequencies ranging from 0.1 Ah, 0.01 Ah and 0.001 Ah before proceeding with reconstruction. Sandia National Laboratories open source data is considered for verifying the proof of concept. Pearson correlation coefficient is deployed to differentiate strong and weak SLB based on sampled data. The actual data obtained from battery cycler fails to do the same and hence, the sampled data aids repurposer in reducing the charging and discharging time of SLB through narrowed voltage bands.

Aging Effects Observed in Automotive Fuel Cell Stacks by Applying a New Realistic Test Protocol and Humidity Control

ABSTRACTTraditional automotive proton exchange membrane fuel cell (PEMFC) endurance testing relies on the fuel cell (FC) dynamic load cycle (FC‐DLC) protocol, which inadequately reflects real‐world driving conditions. To address this limitation the “Investigations on degradation mechanisms and Definition of protocols for PEM Fuel cells Accelerated Stress Testing” (ID‐FAST) consortium defined the new representative “ID‐FAST driving load cycle,” a novel approach capturing the load distribution, transitions, temperature variations, and humidity fluctuations experienced by FCs in real‐world operation. We demonstrate the ID‐FAST driving cycle itself and the integration into a realistic durability test program for FC test benches and present the resulting test data. Furthermore, we showcase its implementation within an accelerated stress testing (AST) protocol, highlighting its potential to significantly reduce testing time by accelerating degradation. Additionally, a novel method for highly dynamic humidity adjustment within test benches is introduced. By overcoming limitations of existing methods and incorporating the ID‐FAST driving cycle, this work paves the way for a new era of efficient and realistic FC endurance testing, ultimately contributing to the development of more robust and durable automotive FC stacks.

A Visual Acuity Assessment System Based on Static Gesture Recognition and Naive Bayes Classifier

Visual Acuity (VA) assessment is crucial for early vision screening, yet traditional methods are manual and time-consuming. Despite the advancements in human-computer interaction (HCI), there is no existing system fully addresses accuracy, efficiency and adaptability. This study introduces an intelligent VA assessment system that combines MediaPipe-based static gesture recognition with a novel Naive Bayes Classifier (NBC)-based VA Thresholds Determination (VATD) scheme. This integration offers a non-contact, user-friendly approach for rapid and precise VA testing. The VATD scheme is designed to significantly reduce the number of test trials; thereby, substantially improving the efficiency. Experimental validation confirms the system's high accuracy (96.72%) within a ±0.1 deviation from standard methods, achieves a 68% reduction in test time compared to traditional methods, and offers a 27% efficiency improvement over ANN-based systems. This system promises to enhance VA assessments, particularly for children and adolescents, with its speed, accuracy, and broader applicability.

Exploring Head-Mounted Virtual Visual Fields for Glaucoma: How do They Stack Up Against Humphrey Tests?

Abstract The integration of virtual reality (VR) technology into visual field (VF) testing has introduced innovative head-mounted perimetry devices, which offer numerous advantages over traditional methods. This review examines the features, accuracy, and reliability of commercially available head-mounted VR perimeters and compares them with the gold standard, the Humphrey Visual Field Analyzer (HFA). While traditional devices like the HFA and Goldmann Perimeter have been reliable for glaucoma management, they are limited by fixed testing environments, long testing durations, and high costs. VR-based perimetry addresses these limitations by providing portable, cost-effective, and engaging testing environments that can be conducted in various settings, including patients’ homes. Head-mounted displays (HMDs) equipped with high-resolution screens, motion sensors, and eye-tracking technology create an immersive testing experience that minimizes distractions and external light interference, potentially enhancing test accuracy. These devices also offer customizable testing protocols, improving patient comfort and compliance, especially among children and elderly patients. However, concerns remain about the reproducibility and accuracy of VR perimetry compared to established standards. Variability among VR platforms and the lack of large normative databases for visual fields pose challenges to their widespread adoption. This review highlights the need for further validation studies and the development of standardized testing protocols to ensure the reliability and accuracy of VR perimetry. Despite these challenges, the flexibility, reduced testing times, and potential for telemedicine applications make VR head-mounted perimetry a promising tool for improving glaucoma management and expanding access to VF testing in diverse clinical environments.

Optimized characterization of the permanent deformation of unbound soils and granular materials considering the master curve concept

This paper presents a new method to evaluate the permanent deformation (PD) of soils and granular materials. Based on the concepts of shakedown and shift model, the PD analysis was proposed by means of master curves considering the initial 10,000 loading cycles of each applied stress state. Analogous to the superposition principle adopted in the construction of master curves for viscoelastic properties, the superposition loading cycle-magnitude of stress pair is proposed for the PD characterization of soils and granular materials. Thus, shift factors are used that provide the construction of master curves for each stress pair in an extended space of cycles. The master curves were used to predict the permanent deformation of soils and granular materials by increasing the number of cycles analyzed for each stress pair. For this, 24 different soils and granular materials were tested and analyzed to identify the influence of the loading cycles and stress state on the material behavior. The predictions provided by the proposed model were compared with those of another model currently used for flexible pavement design in Brazil. The results demonstrated the accuracy of the proposed methodology and indicated that it is possible to significantly reduce testing time than required for the PD characterization of soils and granular materials.

Quick Estimation of Minimum Hearing Levels Using a Binaural Multifrequency Stimulus Paradigm: Proof of Concept.

Objective estimation of minimum hearing levels using auditory brainstem responses (ABRs) elicited by single frequency tone-bursts presented monaurally is currently considered the gold standard. However, the data acquisition time to estimate thresholds (for both ears across four audiometric frequencies) using this method usually exceeds the sleep time (ranging between 35 and 49 minutes) in infants below 4 months, thus providing incomplete information of hearing status which in turn delays timely clinical intervention. Alternate approaches using faster rate, or tone-burst trains have not been readily accepted due to additional hardware and software requirements. We propose here a novel binaural multifrequency stimulation paradigm wherein several stimuli of different frequencies are presented binaurally in an interleaved manner. The rationale here is that the proposed paradigm will increase acquisition efficiency, significantly reduce test time, and improve accuracy by incorporating an automatic wave V detection algorithm. It is important to note that this paradigm can be easily implemented in most commercial ABR systems currently used by most clinicians. Using this binaural multifrequency paradigm, ear specific ABRs were recorded in 30 normal-hearing young adults to both tone-bursts, and narrow-band (NB) iChirps at 500, 1000, 2000, and 4000 Hz. Comparison of ABRs elicited by tone-bursts and narrow-band chirps allowed us to determine if NB iChirps elicited a more robust wave V component compared with the tone-bursts. ABR data were characterized by measures of minimum hearing levels; wave V amplitude; and response detectability for two electrode configurations (high forehead-C7; and high forehead-linked mastoids). Consistent with the research literature, wave V response amplitudes were relatively more robust for NB iChirp stimuli compared with tone-burst stimuli. The easier identification and better detectability of wave V for the NB iChirps at lower stimulus levels contributed to their better thresholds compared with tone-burst elicited responses. It is important to note that binaural multifrequency hearing levels close to minimum hearing levels were determined in approximately 22 minutes using this paradigm-appreciably quicker than the 45 to 60 minutes or longer time required for threshold determination using the conventional single frequency method. Our novel and simple paradigm using either NB iChirps or tone-bursts provides a reliable method to rapidly estimate the minimum hearing levels across audiometric frequencies for both ears. Incorporation of an automatic wave V detection algorithm increases objectivity and further reduce test time and facilitate early hearing identification and intervention.

Research on gear life reliability analysis based on accelerated life test

AbstractAs one of the key components of marine reducer, the life reliability of gear is of great significance to the safe operation of the reducer and even the whole transmission system. However, due to economic and testing constraints, executing extensive fatigue life tests on complex and costly gears presents a formidable challenge, resulting in inadequate reliability assessments. In this paper, the gear life reliability analysis method based on the accelerated life test is proposed. By conducting the gear bending fatigue accelerated life test, the life distribution and failure mechanism are determined based on the accelerated life data. Furthermore, the gear life distribution parameters and acceleration model are solved by least squared method. This allows us to derive a reliability index specific to the service conditions, facilitating the evaluation of gear life reliability. The method proposed in this paper provides data and theoretical support for the accelerated life tests, lays a foundation for the gear reliability evaluation under small samples, and improves the efficiency of the gear fatigue life evaluation. It has significant engineering significance in saving test costs, reducing test times and shortening development cycle.

Enhancing defect detection in solid materials using low-duty cycle square wave lock-in thermography: a time-efficient and quantitative approach

Lock-in thermography has been used to detect material defects but has faced challenges in quantitatively identifying defects. Traditional methods with a sine wave or symmetrical square wave heat source require time-consuming frequency adjustments with multiple experiments. This study introduces a novel technique using a heat source of a low-duty cycle square wave in lock-in thermography. This harmonic-rich heat source provides information on the blind frequency and the depth of defects, significantly reducing testing time. This method was validated by finite element simulations, which generated phase maps and phase difference-frequency curves at multiple frequencies that located defects. The study explored the correlation between thermal diffusion length at the blind frequency and defect depth and the impact of transverse thermal diffusion on depth measurement. The findings indicate that phase maps at the optimal frequency are most suitable for measuring defect edges. This approach offers a promising, time-efficient, and quantitative solution for defect detection.

A new binary chaos-based metaheuristic algorithm for software defect prediction

Software defect prediction is a critical challenge within software engineering aimed at enhancing software quality by proactively identifying potential defects. This approach involves selecting defect-prone modules ahead of the testing phase, thereby reducing testing time and costs. Machine learning methods provide developers with valuable models for categorising faulty software modules. However, the challenge arises from the numerous elements present in the training dataset, which frequently reduce the accuracy and precision of classification. Addressing this, selecting effective features for classification from the dataset becomes an NP-hard problem, often tackled using metaheuristic algorithms. This study introduces a novel approach, the Binary Chaos-based Olympiad Optimisation Algorithm, specifically designed to select the most impactful features from the training dataset. By selecting these influential features for classification, the precision and accuracy of software module classifiers can be notably improved. The study's primary contributions involve devising a binary variant of the chaos-based Olympiad optimisation algorithm to meticulously select effective features and construct an efficient classification model for identifying faulty software modules. Five real-world and standard datasets were utilised across both the training and testing phases of the classifier to evaluate the proposed method's effectiveness. The findings highlight that among the 21 features within the training datasets, specific metrics such as basic complexity, the sum of operators and operands, lines of code, quantity of lines containing code and comments, and the sum of operands have the most significant influence on software defect prediction. This research underscores the combined effectiveness of the proposed method and machine learning algorithms, significantly boosting accuracy (91.13%), precision (92.74%), recall (97.61%), and F1 score (94.26%) in software defect prediction.