Test Code Research Articles

Test code refactoring unveiled: where and how does it affect test code quality and effectiveness?

Test code refactoring unveiled: where and how does it affect test code quality and effectiveness?

QPSK circuit optimization Based on Model-Based Design

In order to address the difficult problem of hardware implementation of complex communication algorithm, this paper takes digital phase modulation as an example, and uses model-based design approach to quickly complete the algorithm modeling, simulation and automatic generation of HDL code. Model-based design is a fast and effective design process for algorithm modeling, simulation, testing and automatic code generation. The simulation results show that the model-based design method cannot only quickly accomplish the modeling and testing of the algorithm, but also improve the design accuracy.

A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.

Skin diseases are a significant global public health concern, affecting 21-85% of the world's population, particularly those in low- and middle-income countries. Accurate and timely diagnosis is crucial for effective treatment and improved patient outcomes. This study introduces a novel deep-learning multi-model architecture designed for high-precision skin disease diagnosis. The system employs a five-category Xception model to classify skin lesions into five classes: Atopic Dermatitis, Acne and Rosacea, Skin Cancer, Bullous, and Others. Trained on 25,010 images, the model achieved 95% accuracy and an AUROC of 99.4%. To further enhance accuracy, transfer learning was applied, resulting in specialized models for each class, with strong performance across 40 skin conditions. Specifically, the Acne and Rosacea model achieved an accuracy of 90.0%, with a precision of 90.7%, recall of 90.1%, f1-score of 90.2%, and an AUROC of 99.0%. The Skin Cancer model demonstrated 94.0% accuracy, 94.8% precision, 94.2% recall, 94.1% f1-score, and a 99.5% AUROC. The Atopic Dermatitis model reported 91.8% accuracy, 92.2% precision, 91.8% recall, 91.9% f1-score, and a 98.8% AUROC. Finally, the Bullous model showed 90.0% accuracy, 90.6% precision, 90.0% recall, 90.0% f1-score, and a 98.9% AUROC. This approach surpasses previous studies, offering a more comprehensive diagnostic tool for skin diseases. To facilitate result reproducibility, the training and testing codes for the models utilized in this study are accessible via the GitHub repository ( https://github.com/SaraEl-Metwally/A-Multi-Model-Deep-Learning-for-Diagnosing-Skin-Diseases ).

Exploring code portability solutions for HEP with a particle tracking test code.

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools-including compiler pragma-based approaches, abstraction libraries, and other tools-allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions. While in several cases portable implementations perform close to the reference code version, we find that the performance varies significantly depending on the details of the implementation. Achieving optimal performance is not easy, even for relatively simple applications such as the test codes considered in this work. Several factors can affect the performance, such as the choice of the memory layout, the memory pinning strategy, and the compiler used. The compilers and tools are being actively developed, so future developments may be critical for their deployment in HEP experiments.

Experimental and numerical study of shear strength on the slide zone soil by the coupling of seepage and shear test

The stability of reservoir landslides is determined by the strength of the slide zone. Using both experimental and numerical techniques to analyze the strength of the slide zone has been widely used in the research of reservoir landslides. However, few models consider the strength of the coupling between seepage and shear. This paper aims to analyze the shear strength of slide zone soil combined with different stresses by using the directed shear test and particle flow code PFC2D. The results indicated that slide zone soil clearly experienced the strain-hardening without seepage. While a specific strain-softening phenomenon was observed because the introduction of seepage reduced the shear strength significantly, the internal friction angle demonstrated a higher sensitivity to seepage stress compared to cohesion as the seepage stress increased. The direction and amount of flow velocity dynamically alter during the shear process, as revealed by numerical simulation results, suggesting that the shear strength was nonlinear with increasing seepage stress and changed structure. It can be assumed that seepage deterioration in the Huangtupo landslide is a dynamic process that influences the structure, strength and permeability characteristics.

Temporal trends and practice variation of paediatric diagnostic tests in primary care: retrospective analysis of 14 million tests

ObjectiveThe primary objective was to investigate temporal trends and between-practice variability of paediatric test use in primary care.Methods and analysisThis was a descriptive study of population-based data from Clinical Practice...

Understanding Test Convention Consistency as a Dimension of Test Quality

Unit tests must be readable to help developers understand and evolve production code. Most existing test quality metrics assess test code's ability to detect bugs. Few metrics focus on test code's readability. One standard approach to improve readability is the consistent application of conventions. We investigated test convention consistency as a dimension of test quality. We formalized test suite consistency as the extent to which alternatives are used within a code base and introduce two complementary metrics to capture this extent. We elaborated a catalog of over 30 test conventions for the Java language organized in 10 convention classes that group mutual alternatives. We developed tool support to detect occurrences of conventions, compute consistency metrics over a test suite, and view occurrences of conventions in the corresponding code. We applied our tools to study the consistency of the test suites of 20 large open-source Java projects. The study validates the design of the test convention classes, provides descriptive statistics on the range of consistency values for ten different convention classes, and enables us to link observed changes in consistency values to specific events in the change history of our target systems, thus providing evidence of the construct validity of the metrics. We conclude that analyzing test suite consistency via static analysis shows promise as a practical approach to help improve test suite quality.

Testing the thermal performance of water cooling towers

Abstract The essential service water system (ESWS) in nuclear power plants circulates the water that cools fan coolers, cooling water heat exchangers, and condensers and other components before dissipating the heat into the environment. Because this includes cooling the systems that remove decay heat from both the primary system and the spent fuel rod cooling ponds, the ESWS is a safety-critical system. In locations without a large body of water in which to dissipate the heat, water is recirculated via a cooling tower. Cooling towers fall into two main categories: Natural draft and Mechanical draft. Since, the mechanical draft cooling towers are much more widely used; the focus is on them in this work to measure its performance. Therefore; the present work addresses the thermal acceptance test for mechanical draft water cooling tower in order to determine its thermal capability in accordance with the cooling technology institute (CTI) code recommendations. Both the Characteristic Curve and the Performance Curve Methods are used. A detailed description of the test procedure according to the CTI ATC-105 test code is presented, and the test code is applied on the Egypt second research reactor (ETRR-2) cooling tower as a case study. All the instruments used in the reactor plant are calibrated prior the test and the measured data is collected at regular intervals according to the code recommendations. The test result shows a tower capability of about 105 % and so the tower is thermally accepted.

Field Performance Testing and Test Codes for Gas Compressors

Abstract Field testing of gas compressor packages requires the accurate determination of efficiency, capacity, head, and power in sometimes less than ideal working environments. The results of field tests have significant implications for the compressor manufacturers and their customers, as economic considerations demand that the performance and efficiency of an installation are verified to assure a project's return on investment. Thus, for the compressor manufacturers, as well as for the end-user, an accurate determination of the compressor performance in the field is of vital interest. While field performance tests have to be executed under conditions dictated by the situation at site, factory test codes like ASME PTC10 and ISO 5389 provide guidance. The former code was recently revised, so a discussion of this revised code in the context of site performance testing is appropriate. A key change in PTC10 is the method to calculate polytropic work, which affects data reduction. It also affirms a method to calculate error propagation and test uncertainties. The discussion includes the appropriate use of Equations of State, and the definition of steady state requirements. Different methods to define valid operating conditions for the test, based on similarity considerations are compared. However, field tests, unlike factory tests, are ultimately conducted based on the conditions at site which may or may not conform with test codes, Therefore, calculation of test uncertainties, and the use of observations not covered by the codes are emphasized. The paper attempts to provide guidance for the conduct of field performance tests.

A-236 Rapid multiplexed PCR (polymerase chain reaction) tests for respiratory disease improve process and time to diagnosis versus laboratory-based molecular testing

Abstract Background The Infectious Disease Society of America recommends molecular testing for symptomatic patients suspected of having COVID-19. Molecular tests can be performed at the point of care or in a centralized laboratory, using different methodologies, and can be singleplex or multiplex. Limited data are available examining the use of these tests in clinical practice and impact of different test types on patient outcomes; however, common antiviral treatments for both COVID-19 and influenza are more effective when taken earlier and are not indicated for use more than 5 and 2 days after symptom onset, respectively. The aim of this study was to describe the diagnostic journey for non-inpatients who received molecular testing for respiratory pathogens in the laboratory versus those who received testing using a rapid, multiplexed real-time RT-PCR test that can be performed at the point of care. Methods De-identified administrative claims were analyzed from July 1, 2020 through September 30, 2022 for commercially insured and Medicare Advantage enrollees in the Optum Labs Data Warehouse. Patients with ≥1 medical claim for a non-inpatient visit with a procedure code for testing for SARS-CoV-2 were identified; patients who received antigen testing at index were excluded. Patients were followed for up to 90 days. Two patient groups were selected for the final study sample: 1) Patients with ≥1 claim with CPT code 0240U or 0241U with a point-of-care or laboratory place of service on the index date (proprietary laboratory analyses codes for Xpert® Xpress CoV-2/Flu/RSV, or “Xpert Xpress”); and 2) Patients with other laboratory-based molecular testing on the index date. Standardized difference of >0.10 or <-0.10 was considered a significant imbalance in proportions or means. Results In total, 51,602 patients received testing with Xpert Xpress while 317,192 received laboratory testing. Patients who were tested with Xpert Xpress were significantly older (mean 49.82 vs 42.19, SD 0.3), had a higher Charlson Comorbidity Index (mean 0.7 vs 0.35, SD 0.29), and were more likely to have a respiratory risk factor (6.09% vs 2.38%, SD 0.19). On average, patients who were tested with Xpert Xpress had significantly fewer tests for SARS-CoV-2 on the index date (mean 1.2 vs 1.58, SD -0.55), and fewer tests for SARS-CoV-2, influenza, or RSV on the index date and through the end of the 90-day fixed follow-up period (mean 1.64 vs 2.57, SD -0.45). They were also significantly more likely to be diagnosed with COVID-19 on the index date (23.31% vs 16.53%, SD 0.17). Among patients with a diagnosis for COVID-19, mean time to diagnosis was significantly shorter among patients tested with Xpert Xpress (4.85 vs 8.86 days, SD -0.27; median 0 vs 4 days). This finding was also consistent for influenza (4.05 vs 20.28 days, SD -0.74; median 0 vs 5 days). Conclusions Testing with Xpert Xpress at index resulted in fewer total tests and faster diagnosis compared to testing in the laboratory. Rapid, multiplex PCR testing provides a faster time to diagnosis and may support more appropriate and effective treatment.

AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content

Amidst the burgeoning information age, the rapid development of artificial intelligence-generated content (AIGC) has brought forth challenges regarding information authenticity. The proliferation of distorted information significantly impacts users negatively. This study aims to systematically categorize distorted information within AIGC, delve into its internal characteristics, and provide theoretical guidance for its management. Utilizing ChatGPT as a case study, we conducted empirical content analysis on 243 instances of distorted information collected, comprising both questions and answers. Three coders meticulously interpreted each instance of distorted information, encoding error points based on a predefined coding scheme and categorizing them according to error type. Our objective was to refine and validate the distorted information category list derived from the review through multiple rounds of pre-coding and test coding, thereby yielding a comprehensive and clearly delineated category list of distorted information in AIGC. The findings identified 8 first-level error types: “Overfitting”; “Logic errors”; “Reasoning errors”; “Mathematical errors”; “Unfounded fabrication”; “Factual errors”; “Text output errors”; and “Other errors”, further subdivided into 31 second-level error types. This classification list not only lays a solid foundation for studying risks associated with AIGC but also holds significant practical implications for helping users identify distorted information and enabling developers to enhance the quality of AI-generated tools.

CIPerf: a benchmark for continuous integration services performance and cost analysis

Continuous Integration is a crucial practice in modern software development, enabling teams to automate the process of building, testing, and merging code increments to ensure continuous delivery of high-quality software. Despite its growing adoption, the cost and performance of Continuous Integration services often go unexamined in sufficient detail. This paper presents CIPerf, a comprehensive benchmark designed to analyze both the performance and cost of cloud-based and self-hosted Continuous Integration services. The study centers on a comparison between two specific services: Bitbucket Pipelines, a cloud-based offering by Atlassian, and Hetzner, a self-hosted solution. By focusing on these platforms, the research aims to provide practical insights into the real-world costs and execution performance of Continuous Integration services. To achieve this, CIPerf conducted automated tests on an hourly basis over a two-month period, measuring critical timeframes such as resource provisioning, environment setup, and the actual test execution times. The results showed significant differences in both the cost efficiency and the consistency of performance between the two services. For instance, Bitbucket Pipelines, while convenient in its cloud-based offering, demonstrated higher variability in provisioning times compared to the stable, predictable performance of Hetzner’s self-hosted environment. The analysis also explored how these performance metrics influence key software development metrics, including deployment frequency and developer productivity. CIPerf provides a clear methodology for developers and organizations to objectively assess their Continuous Integration service options, ultimately helping them optimize their workflows. Moreover, this benchmark can serve as an ongoing tool to monitor service performance over time, identifying potential degradations or improvements in service quality, thus offering longterm value for teams that rely on Continuous Integration for their development processes.

Review and analysis of software simulators for robotic information systems

The rapid development of educational technologies requires the active involvement of new means and effective teaching methods. One of the innovative tools of learning in the field of robotics is the use of robot simulators in the educational process, which open up many opportunities for better assimilation of knowledge and development of practical skills among students. Robot simulators are software complexes that allow you to visualize and simulate the behavior of real robots. With these tools, students can: program robots in a virtual environment, test code and debug without the risk of damaging real equipment; to study robotics and the principles of robot operation, exploring different types of sensors, actuators and algorithms of robot behavior; to solve problems in various fields, such as production automation, logistics, construction, etc.; practices your skills in a safe and controlled environment. Among the benefits of using robotic simulators are next: availability for a large number of students, as simulators are much cheaper than real jobs; safety, as the virtual environment eliminates the risks associated with working with real robots; flexibility that allows students to work at their own pace, explore different scenarios and experiment with different parameters; interactivity with a realistic 3D-environment and visualization. This work presents an overview and analysis of software that allows modeling robot control systems and simulating their operation for various algorithms. A review and analysis of the proposed software tools for simulating robotic systems, which are used to teach bachelor's and master's degree students in the specialties of "Computer Science", "Information Systems", "Industrial Mechanical Engineering" and "Applied Mechanics" and have proven their effectiveness and effectiveness in distance learning at the Kyiv National University of Construction and Architecture.

Impact of COVID-19 on myalgic encephalomyelitis/chronic fatigue syndrome-like illness prevalence: A cross-sectional survey.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) can be triggered by infectious agents including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the impact of the coronavirus disease 2019 (COVID-19) pandemic on ME/CFS prevalence is not well characterized. In this population-based cross-sectional study, we enrolled a stratified random sample of 9,825 adult participants in the Kaiser Permanente Northern California (KPNC) integrated health system from July to October 2022 to assess overall ME/CFS-like illness prevalence and the proportion that were identified following COVID-19 illness. We used medical record and survey data to estimate the prevalence of ME/CFS-like illness based on self-reported symptoms congruent with the 2015 Institute of Medicine ME/CFS criteria. History of COVID-19 was based on a positive SARS-CoV-2 nucleic acid amplification test or ICD-10 diagnosis code in the medical record, or self-report of prior COVID-19 on a survey. Of 2,745,374 adults in the eligible population, an estimated 45,892 (95% confidence interval [CI]: 32,869, 58,914) or 1.67% (CI 1.20%, 2.15%) had ME/CFS-like illness. Among those with ME/CFS-like illness, an estimated 14.12% (CI 3.64%, 24.6%) developed the illness after COVID-19. Among persons who had COVID-19, those with ME/CFS-like illness after COVID-19 were more likely to be unvaccinated and to have had COVID-19 before June 1, 2021. All persons with ME/CFS-like illness had significant impairment in physical, mental, emotional, social, and occupational functioning compared to persons without ME/CFS-like illness. In a large, integrated health system, 1.67% of adults had ME/CFS-like illness and 14.12% of all persons with ME/CFS-like illness developed it after COVID-19. Though COVID-19 did not substantially increase ME/CFS-like illness in the KPNC population during the study time period, ME/CFS-like illness nevertheless affects a notable portion of this population and is consistent with estimates of ME/CFS prevalence in other populations. Additional attention is needed to improve awareness, diagnosis, and treatment of ME/CFS.

GTAM: a molecular pretraining model with geometric triangle awareness.

Molecular representation learning is pivotal for advancing deep learning applications in quantum chemistry and drug discovery. Existing methods for molecular representation learning often fall short of fully capturing the intricate interactions within chemical bonds of 2D topological graphs and the multifaceted effects of 3D geometric conformations. To overcome these challenges, we present a novel contrastive learning strategy for molecular representation learning, named Geometric Triangle Awareness Model (GTAM). This method integrates innovative molecular encoders for both 2D graphs and 3D conformations, enabling the accurate capture of geometric dependencies among edges in graph-based molecular structures. Furthermore, GTAM is bolstered by the development of two contrastive training objectives designed to facilitate the direct transfer of edge information between 2D topological graphs and 3D geometric conformations, enhancing the functionality of the molecular encoders. Through extensive evaluations on a range of 2D and 3D downstream tasks, our model has demonstrated superior performance over existing approaches. The test code and data of GTAM are available online at https://github.com/StellaHxy/GTAM.

Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System.

Indoor motion planning challenges researchers because of the high density and unpredictability of moving obstacles. Classical algorithms work well in the case of static obstacles but suffer from collisions in the case of dense and dynamic obstacles. Recent reinforcement learning (RL) algorithms provide safe solutions for multiagent robotic motion planning systems. However, these algorithms face challenges in convergence: slow convergence speed and suboptimal converged result. Inspired by RL and representation learning, we introduced the ALN-DSAC: a hybrid motion planning algorithm where attention-based long short-term memory (LSTM) and novel data replay combine with discrete soft actor-critic (SAC). First, we implemented a discrete SAC algorithm, which is the SAC in the setting of discrete action space. Second, we optimized existing distance-based LSTM encoding by attention-based encoding to improve the data quality. Third, we introduced a novel data replay method by combining the online learning and offline learning to improve the efficacy of data replay. The convergence of our ALN-DSAC outperforms that of the trainable state of the arts. Evaluations demonstrate that our algorithm achieves nearly 100% success with less time to reach the goal in motion planning tasks when compared to the state of the arts. The test code is available at https://github.com/CHUENGMINCHOU/ALN-DSAC.

Optimizing regression testing with AHP-TOPSIS metric system for effective technical debt evaluation

Regression testing is essential to ensure that the actual software product confirms the expected requirements following modification. However, it can be costly and time-consuming. To address this issue, various approaches have been proposed for selecting test cases that provide adequate coverage of the modified software. Nonetheless, problems related to omitting and/or rerunning unnecessary test cases continue to pose challenges, particularly with regard to technical debt (TD) resulting from code coverage shortcomings and/or overtesting. In the case of testing-related shortcomings, incurring TD may result in cost and time savings in the short run, but it can lead to future maintenance and testing expenses. Most prior studies have treated test case selection as a single-objective or two-objective optimization problem. This study introduces a multi-objective decision-making approach to quantify and evaluate TD in regression testing. The proposed approach combines the analytic-hierarchy-process (AHP) method and the technique of order preference by similarity to an ideal solution (TOPSIS) to select the most ideal test cases in terms of objective values defined by the test cost, code coverage, and test risk. This approach effectively manages the software regression testing problems. The AHP method was used to eliminate subjective bias when optimizing objective weights, while the TOPSIS method was employed to evaluate and select test-case alternatives based on TD. The effectiveness of this approach was compared to that of a specific multi-objective optimization method and a standard coverage methodology. Unlike other approaches, our proposed approach always accepts solutions based on balanced decisions by considering modifications and using risk analysis and testing costs against potential technical debt. The results demonstrate that our proposed approach reduces both TD and regression testing efforts.

Interpreting pathology test result values with comparators (< , >) in Electronic Health Records research: an OpenSAFELY short data report

Background Numeric results of pathology tests are sometimes returned as a range rather than a precise value, e.g. “<10”. In health data research, test result values above or below clinical threshold values are often used to categorise patients into groups; however comparators (<, > etc) are typically stored separately to the numeric values and often ignored, but may influence interpretation. Methods With the approval of NHS England we used routine clinical data from 24 million patients in OpenSAFELY to identify pathology tests with comparators commonly attached to result values. For each test we report: the proportion returned with comparators present, split by comparator type and geographic region; the specific numeric result values returned with comparators, and the associated reference limits. Results We identified 11 common test codes where at least one in four results had comparators. Three codes related to glomerular filtration rate (GFR) tests/calculations, with 31-45% of results returned with “≥” comparators. At least 90% of tests with numeric values 60 and 90 represented ranges (≥60 and ≥90 respectively) rather than exact values. The other tests - four blood tests (Nucleated red blood cell count, Plasma C reactive protein, Tissue transglutaminase immunoglobulin A, and Rheumatoid factor), two urine tests (albumin/microalbumin) and two faecal tests (calprotectin and quantitative faecal immunochemical test) - were returned with “≤” comparators (29-86%). Conclusions Comparators appear commonly in certain pathology tests in electronic health records. For most common affected tests, we expect there to be minimal implications for researchers for most use-cases. However, care should be taken around whether results falling exactly on clinical threshold values should be considered “normal” or “abnormal”. Results from GFR tests/calculations cannot reliably distinguish between mild kidney disease (stage G2, 60-<90) versus healthy kidney function (90+). More broadly, health data researchers using numeric test result values should consider the impact of comparators.

Experimental Structural Safety Analysis of Front-End Loader of Agricultural Tractor

The agricultural front-end loader is an implement attached to the front of tractors to transport various agricultural materials, including soil. Since they are subjected to various loads due to the working environment, their safety analysis in consideration of actual working conditions is required. However, there are no official standardized test codes to consider various actual working environments currently. In this study, the structural safety of a front-end loader for static and fatigue failures was evaluated using new test code reflecting actual working environments. Thirty-four measurement locations were determined as the stress concentration spots of each component of the front-end loader derived through multibody dynamic simulation. The total testing time was set to 1 h, and the test time for each task was determined considering the duty percentage of the actual loader work. The measurement results showed that the maximum stress that exceeds the material’s yield strength occurred at two locations of the mount, which is the connection to the tractor body, resulting in static yielding. For tasks, the pulling and dumping exhibited the highest stress. The task that had the largest impact on fatigue damage was the dumping. The static safety factor was found to be over 1.93 and the fatigue life met the required lifespan at all measurement locations except for those exhibiting static yielding. Therefore, the most vulnerable part of the front-end loader is the mount, and it is necessary to secure the overall structural integrity by robust design for the mount.

A Technique for In-Situ Displacement and Strain Measurement with Laboratory-Scale X-Ray Computed Tomography

Abstract Purpose: Establish a technique for simultaneous interrupted volumetric imaging of internal structure and time-resolved full-field surface strain measurements during in-situ X-ray micro-computed tomography (XCT) experiments. This enables in-situ testing of stiff materials with large forces relative to the compliance of the in-situ load frame, which might exhibit localization (e.g., necking, compaction banding) and other inhomogeneous behaviors.Methods: The system utilizes a combination of in-situ XCT, 2D X-ray imaging, and particle tracking to conduct volumetric imaging of the internal structure of a specimen with interrupted loading and surface strain mapping during loading. Critically, prior to the laboratory-scale XCT experiments, specimens are speckled with a high-X-ray-contrast powder that is bonded the surface. During in-situ loading, the XCT system is programmed to capture sequential 2D X-ray images orthogonal to the speckled specimen surface. A single particle tracking (SPT) or digital image correlation (DIC) algorithm is used to measure full-field surface strain evolution throughout the time-sequence of images. At specified crosshead displacements, the motion and 2D image sequence is paused for volumetric XCT image collection. Results: We show example results on a micro-tensile demonstration specimen additive manufactured from Inconel 718 nickel-chrome alloy. Results include XCT volume reconstructions, crosshead-based engineering stress, and full-field strain maps. Conclusion: We demonstrate an in-situ technique to obtain surface strain evolution during laboratory-scale XCT testing and interrupted volumetric imaging. This allows closer investigation of, for example, the effect of micro-pores on the strain localization behavior of additive manufactured metal alloys. In addition to describing the method using a representative test piece, the dataset and code are published as open-source resources for the community. Graphical abstract