Reduce Test Cost Research Articles

A general method for determining probabilistic S-N curve based on static strength data of material

The conventional group test method for obtaining probabilistic S- N curve is a time-consuming and expensive test program. This study aims to present an alternative method for determining the probabilistic S- N curve of material based on its static strength data. Firstly, the probabilistic mapping relationship between the static strength and fatigue life of material is elaborated. Subsequently, the Weibull distribution is utilized to model the static strength and fatigue life data of material, and the correlation between the distribution parameters of them is investigated. Finally, an alternative method for determining the probabilistic S- N curve of material is proposed, and the experimental data of carbon steels and composite laminates are applied to verify its validity. The results show that the proposed method is capable of determining the probabilistic S- N curve of material based on its static strength data, which can significantly save test time and reduce test cost. In engineering applications, when the parameters of static strength distribution and the median S- N curve are known, the probabilistic S- N curve with any given survival probability can be determined through a unified analytical expression.

On-Chip Structures for Fmax Binning and Optimization.

Process variations during manufacturing lead to differences in the performance of the chips. In order to better utilize the performance of the chips, it is necessary to perform maximum operation frequency () tests to place the chips into different speed bins. For most tests, significant efforts are put in place to reduce test cost and improve binning accuracy; e.g., our conference paper published in ICICM 2017 presents a novel binning sensor for low-cost and accurate speed binning. However, by promoting chips placed at the lower bins, because of conservative binning, into higher bins, the overall profit can greatly increase. Therefore, this paper, extended based on a conference paper, presents a novel and adaptive methodology for speed binning, in which the paths impacting the speed bin of a specific IC are identified and adapted by our proposed on-chip Binning Checker and Binning Adaptor. As a result, some parts at a bin margin can be promoted to higher bins. The proposed methodology can be used to optimize the yield of a digital circuit when it has redundant timing in clock tree, and it can be integrated into current tests with low extra cost. The proposed adaptive system has been implemented and validated on five benchmarks from ITC, ISCAS89, and OpenSPARCT2 core on 28 nm Altera FPGAs. Measurement results show that the number of higher bin chips is improved by 7–16%, and our cost analysis shows that the profit increase is between 1.18% and 3.04%.

The AHP method in the optimization of the epoxidation of allylic alcohols

The article presents the results of work on the application of the AHP method in the selection of optimal parameters of the epoxidation process of unsaturated compounds, which significantly affect the course of the research process, while allowing the user to ignore those factors that have a negligible impact on the results. AHP is a multi-criteria decision technique that can assist in making an overall decision by breaking down a complex problem into a multi-level hierarchical structure of goal, criteria, and alternatives. It has been found that this method greatly simplifies the testing process, reducing the time to obtain reliable results and reducing testing costs as it reduces the number of tests required.

An EFSM-Based Test Data Generation Approach in Model-Based Testing

Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obsolete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model (EFSM). The proposed approach integrates MBT with combinatorial testing. The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach. The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventional MB-TCG but at the same time generated 43 more tests. The proposed approach effectively detects faults, but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.

Master-slave based test cost reduction method for DNN accelerators

Master-slave based test cost reduction method for DNN accelerators

A 1D thermomechanical model for saturated clay and its optimisation-based parameter identification

This paper proposes a one-dimensional (1D) thermomechanical model for clays and its optimisation method for identifying the parameters from only limited laboratory tests. First, a new thermomechanical model accounting for the volumetric expansion and shrinkage behaviour under heating–cooling cycle is proposed for test simulations. Then, the optimisation-based parameter identification is proposed. A newly developed mutation is adopted to enhance the original Nelder–Mead differential evolution algorithm (NMDE). In the optimisation, the laboratory tests on Bangkok clay are selected as objective, with two temperature-controlled oedometer tests and three heating–cooling cycle tests under different overconsolidation ratios (OCRs). The sensitivity of model parameters is analysed by a sensitivity analysis method. Five state-of-the-art optimisation algorithms are chosen as comparison to highlight the effectiveness and efficiency of the proposed NMDE. Finally, the optimal parameters are verified by comparing with other experimental results and predictions on two other clays. All comparisons show that a reliable solution can be acquired by the proposed optimisation together with the thermomechanical model, which is practically useful with a reduction in testing costs.

Valid test pattern identification for VLSI adaptive test

With continuously decreasing circuit scale, more and more test patterns (test contents) are added to test set to achieve acceptable defect levels, which seriously affect test time and, consequently, test cost. Theoretically, dropping invalid (pattern that can make the test pass) test patterns can reduce test cost. In fact, When the valid (pattern that can make the test fail) and invalid patterns overlap seriously, identifying effective patterns performs poorly, which will not achieve the expected results. This paper proposes a kind of eliminate redundancy method, so that this LDA-MRMR algorithm which can provide test cost reduction without increasing the defect level obviously. Experimental results demonstrate that the proposed method sacrifices 3% of the predictive accuracy in exchange for 3.7 times time saving compared with traditional methods. In addition, the algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process.

Implementation of a built-in self-test for nuclear power plant FPGA-based safety-critical control systems

Field-programmable gate arrays (FPGAs) are being widely used in the instrumentation and control (I&C) systems of nuclear power plants (NPPs). However, FPGAs have the possibility of failure, resulting in the reliability not being fully guaranteed; therefore, some methods are required to evaluate and verify their reliability. The built-in self-test (BIST) method is a technology that implants relevant functional circuits into another circuit during design to provide self-test functions. It has many advantages over external test devices. For this study, the FPGA-based reactor protection subsystem of the CPR1000 NPP was selected as the test object. This paper presents the principle of system partition, the main objective of which is to reduce the test time and provide convenience for subsequent modifications; then, we use the BIST method and build the BIST architecture to test each input signal channel as a black box. For several common failures, 100% diagnostic coverage can be obtained, which improves the reliability of a protection system. BIST can be used as a supplement to external tests; it can reduce the test time, reduce testing costs, and solve many problems that cannot be solved using external tests, such as the lack of interfaces. The research results provide theoretical and technical references for the application and review of FPGA-based applications in NPP safety–critical systems.

Screening for androgen agonists using autonomously bioluminescent HEK293 reporter cells.

Due to the public health concerns of endocrine-disrupting chemicals, there is an increasing demand to develop improved high-throughput detection assays for enhanced exposure control and risk assessment. A substrate-free, autobioluminescent HEK293ARE/Gal4-Lux assay was developed to screen compounds for their ability to induce androgen receptor (AR)-mediated transcriptional activation. The assay was validated against a group of 40 recommended chemicals and achieved an overall 87.5% accuracy in qualitatively classifying positive and negative AR agonists. The HEK293ARE/Gal4-Lux assay was demonstrated as a suitable tool for Tier 1 AR agonist screening. By eliminating exogenous substrate, this assay provided a significant advantage over traditional reporter assays by enabling higher-throughput screening with reduced testing costs while maintaining detection accuracy.

Co-relation scan attack analysis (COSAA) on AES: A comprehensive approach

Scan based DfT is indispensable for IC testing in the semiconductor chip industry to ensure correctness of chip, both functionally and structurally. Since a higher degree of fault coverage is essential, cryptographic ICs rely on it as a standard technique during manufacturing test. Yet an invaluable tradeoff needs to be met between security and testability, because it is the observability and controllability of the sequential memory elements in the scan chain which are enhanced to facilitate testing. Well designed differential scan attacks which make use of this feature can prove to be critically pervasive to leak secret keys embedded in cryptochips. Use of response compactors ensure test cost reduction. Existing differential scan attacks target Hamming weight pairs that are unique. The motivation of our work lies in the fact that carefully fabricating additional bits in order to transform a unique Hamming weight to a non-unique one, existing attacks can be defended. In this paper, we propose Co-relation scan attack (COSAA) on AES crypto-system which can work even in the presence of test response compaction. We show our attack technique to be lethal against any possible Hamming weight model which means that the designer needs to prevent the circuit under test from a relatively broader attack surface. Our experiments show the proposed attack can successfully recover the secret key of AES with XOR compaction in 24 milliseconds using an average desktop machine.

Test Scheduling and Test Time Reduction for SoC by Using Enhanced Firefly Algorithm

System-on-Chip (SoC) is an integration of electronic components and billions of transistors. Defects due to the base material is caused during the manufacturing of components. To overcome these issues testing of chips is necessary but total cost increases because of increasing test time. The main issues to be considered during testing of SoC are the time taken for testing and accessibility of core. Effective test scheduling should be done to minimize testing time. In this paper, an effective test scheduling mechanism to minimize testing time is proposed. The test time reduction causes test cost reduction. The Enhanced Firefly algorithm is used in this paper to minimize test time. Enhanced Firefly algorithm gives a better result than Ant colony and Firefly algorithms in terms of test time reduction thereby reduction test cost takes place.

A 24-Hour Extended Calibration Strategy for Quantitating Tacrolimus Concentrations by Liquid Chromatography-Tandem Mass Spectrometry.

Tacrolimus has a low therapeutic index requiring strict control of whole blood concentrations. Although random access immunoassay platforms exist that rapidly provide quantitative values for tacrolimus, LC-MS/MS may provide more accurate quantitation. However, batch testing in many LC-MS/MS assays is not efficient, particularly when testing patients suspected of having tacrolimus toxicity. Extending calibration curve stability beyond the traditionally accepted single batch may facilitate improved turnaround time and reduce testing costs. A 24-h extended calibration of LC-MS/MS tacrolimus was designed and validated to reduce calibrator usage, improve turnaround time, and provide a more efficient workflow for urgent requests. Patient samples included in the study were extracted and assayed with coextracted calibrators and quality control in real time. The same patient samples were extracted again 24 h later without coextracted calibrators. The data acquired from the second patient sample extraction was applied to the original calibration curve acquired 24 h prior and compared to the data for the same samples coextracted with calibrators, creating a value set utilizing extended curve stability. A linear regression compared the results using the extended curve to the results of the coextracted acquisitions. This yielded a strong correlation between the 2 data populations, with a slope of 1.0061 and a correlation coefficient of >0.95. The average bias between original patient values and patient values 24 h later was 3.4% across all patient samples. Patient tacrolimus values were comparable when extracted within 24 h of calibration versus values coextracted with calibrators. Demonstrating comparability within 24 h of calibration allows the laboratory to provide rapid turnaround time for urgent samples without the need for an entirely new calibration curve.

Determining Cost-Optimal Next-Generation Sequencing Panels for Rare Disease and Pharmacogenomics Testing.

Multi-gene panel sequencing using next-generation sequencing (NGS) methods is a key tool for genomic medicine. However, with an estimated 140 000 genomic tests available, current system inefficiencies result in high genetic-testing costs. Reduced testing costs are needed to expand the availability of genomic medicine. One solution to improve efficiency and lower costs is to calculate the most cost-effective set of panels for a typical pattern of test requests. We compiled rare diseases, associated genes, point prevalence, and test-order frequencies from a representative laboratory. We then modeled the costs of the relevant steps in the NGS process in detail. Using a simulated annealing-based optimization procedure, we determined panel sets that were more cost-optimal than whole exome sequencing (WES) or clinical exome sequencing (CES). Finally, we repeated this methodology to cost-optimize pharmacogenomics (PGx) testing. For rare disease testing, we show that an optimal choice of 4-6 panels, uniquely covering genes that comprise 95% of the total prevalence of monogenic diseases, saves $257-304 per sample compared with WES, and $66-135 per sample compared with CES. For PGx, we show that the optimal multipanel solution saves $6-7 (27%-40%) over a single panel covering all relevant gene-drug associations. Laboratories can reduce costs using the proposed method to obtain and run a cost-optimal set of panels for specific test requests. In addition, payers can use this method to inform reimbursement policy.

Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping.

Rising cancer care costs impose financial burdens on health systems. Applying artificial intelligence to diagnostic algorithms may reduce testing costs and avoid wasteful therapy-related expenditures. To evaluate the financial and clinical impact of incorporating artificial intelligence-based determination of mismatch repair/microsatellite instability status into the first-line metastatic colorectal carcinoma setting, we developed a deterministic model to compare eight testing strategies: A) next-generation sequencing alone, B) high-sensitivity polymerase chain reaction or immunohistochemistry panel alone, C) high-specificity panel alone, D) high-specificity artificial intelligence alone, E) high-sensitivity artificial intelligence followed by next generation sequencing, F) high-specificity artificial intelligence followed by next-generation sequencing, G) high-sensitivity artificial intelligence and high-sensitivity panel, and H) high-sensitivity artificial intelligence and high-specificity panel. We used a hypothetical, nationally representative, population-based sample of individuals receiving first-line treatment for de novo metastatic colorectal cancer (N = 32,549) in the United States. Model inputs were derived from secondary research (peer-reviewed literature and Medicare data). We estimated the population-level diagnostic costs and clinical implications for each testing strategy. The testing strategy that resulted in the greatest project cost savings (including testing and first-line drug cost) compared to next-generation sequencing alone in newly-diagnosed metastatic colorectal cancer was using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel for patients testing negative by artificial intelligence ($400 million, 12.9%). The high-specificity artificial intelligence-only strategy resulted in the most favorable clinical impact, with 97% diagnostic accuracy in guiding genotype-directed treatment and average time to treatment initiation of less than one day. Artificial intelligence has the potential to reduce both time to treatment initiation and costs in the metastatic colorectal cancer setting without meaningfully sacrificing diagnostic accuracy. We expect the artificial intelligence value proposition to improve in coming years, with increasing diagnostic accuracy and decreasing costs of processing power. To extract maximal value from the technology, health systems should evaluate integrating diagnostic histopathologic artificial intelligence into institutional protocols, perhaps in place of other genotyping methodologies.

Validation of Predictive Models for Autoimmune Encephalitis-Related Antibodies to Cell-Surface Proteins Expressed in Neurons: A Retrospective Study Based in a Hospital.

Objective: Autoimmune encephalitis (AE) is a severe but treatable autoimmune disorder that is diagnosed by antibody (Ab) testing. However, it is unrealistic to obtain an early diagnosis in some areas since the Ab status cannot be immediately determined due to time and technology restrictions. In our study, we aimed to validate the Antibody Prevalence in Epilepsy and Encephalopathy (APE2) score among patients diagnosed with possible AE as a predictive model to screen AE patients with antibodies to cell-surface proteins expressed in neurons.Methods: A total of 180 inpatients were recruited, and antibodies were detected through serological and/or cerebrospinal fluid (CSF) evaluations. The APE2 score was used to validate the predictive models of AE with autoantibodies.Results: The mean APE2 score in the Ab-positive cases was 7.25, whereas the mean APE2 score in the Ab-negative cases was 3.18 (P < 0.001). The APE2 score had a receiver operating characteristic (ROC) area under the curve of 0.924 [P < 0.0001, 95% confidence interval (CI) = 0.875–0.973]. With a cutoff score of 5, the APE2 score had the best psychometric properties, with a sensitivity of 0.875 and a specificity of 0.791.Conclusion: The APE2 score is a predictive model for AE with autoantibodies to cell-surface proteins expressed in neurons and was validated and shown to have high sensitivity and specificity in our study. We suggest that such a model should be used in patients with a suspected diagnosis of AE, which could increase the detection rate of Abs, reduce testing costs, and help patients to benefit from treatment quickly.

Software-based online self-testing using bounded model checking for 3D network-on-chips

Online testing has become essential for the reliable operations of manycore systems using three-dimension (3D) network-on-chip (NoC) fabric. This paper presents a software-based online self-testing solution using bounded model checking (BMC) for 3D NoCs. The proposed method implements BMC on the extended finite state machine of the 3D NoC and extracts leading sequences to triggerinternal functions. Next, it detects structural defects in each function activated by the leader sequence using constrained ATPG. Finally, it effectively organizes packets to test the 3D NoC and reduce test cost. Experimental results demonstrate that the proposed method achieves high fault coverage online with extremely lower test cost and outperforms existing methods.

Software Batch Testing to Save Build Test Resources and to Reduce Feedback Time

Testing is expensive and batching tests has the potential to reduce test costs. The continuous integration strategy of testing each commit or change individually helps to quickly identify faults but leads to a maximal number of test executions. Large companies that have a massive number of commits, e.g., Google and Facebook, or have expensive test infrastructure, e.g., Ericsson, must batch changes together to reduce the number of total test runs. For example, if eight builds are batched together and there is no failure, then we have tested eight builds with one execution saving seven executions. However, when a failure occurs it is not immediately clear which build is the cause of the failure. A bisection is run to isolate the failing build, i.e., the culprit build. In our eight builds example, a failure will require an additional 6 executions, resulting in a saving of one execution. In this work, we re-evaluate batching approaches developed in industry on large open source projects using Travis CI. We also introduce novel batching approaches. In total, we evaluate six approaches. The first is the baseline approach that tests each build individually. The second, is the existing bisection approach. The third uses a batch size of four, which we show mathematically reduces the number of execution without requiring bisection. The fourth combines the two prior techniques introducing a stopping condition to the bisection. The final two approaches use models of build change risk to isolate risky changes and test them in smaller batches. We find that compared to the TestAll baseline, on average, the approaches reduce the number of <i>build test executions</i> across projects by 46, 48, 50, 44, and 49 percent for BatchBisect, Batch4, BatchStop4, RiskTopN, and RiskBatch, respectively. The greatest reduction in executions is BatchStop4 at 50 percent. However, the simple approach of Batch4 does not require bisection and achieves a reduction of 48 percent. In a larger sample of projects, we find that a project’s failure rate is strongly correlated with execution savings (Spearman <inline-formula><tex-math notation="LaTeX">$r = -0.97$</tex-math></inline-formula> with a <inline-formula><tex-math notation="LaTeX">$p \ll 0.001$</tex-math></inline-formula> ). Using Batch4, 85 percent of projects see savings. All projects that have build failures less than 40 percent of the time will benefit from batching. In terms of <i>feedback time</i> , compared to TestAll, we find that BatchBisect, Batch2, Batch4, BatchStop4 all reduce the average feedback time by 33, 16, 32, and 37 percent. Simple batching saves not only resources but also reduces feedback time without introducing any slip-throughs and without changing the test run order. We suggest that most projects should adjust their CI pipelines to use a batch size of at least two. We release our scripts and data for replication ¹ as well as the <monospace>BatchBuilder</monospace> tool ² that automatically batches submitted changes on GitHub for testing on Travis CI. Since the tool reports individual results for each pull-request or pushed commit, the batching happens in the background and the development process is unchanged.

Analysis and Design Optimization of Permanent Magnet Motor with External Rotor for Direct Driven Mixer

Due to the need for developing technology and comfort today, the use of electric motors increases in different fields such as pumps, household appliances, fans, aerospace and mixers. This affects energy consumption and production costs, and ultimately triggered the development of higher performance motors. Because of the multi-parameter designs and nonlinear structure of electric motor, it has become very important the use of multi-objective optimization algorithms in design studies on electric motors. At the same time, real-time simulations are provided by using finite element analysis programs to reduce test costs. This study purposes analysis and multi-objective design optimization of a direct drive permanent magnet synchronous motor with external rotor for low speed and high torque mixer applications. So, the third-generation algorithm NSGAIII was used and compared with the second-generation algorithm NSGAII. To examine both initial design result and optimization results Ansys/Maxwell program was used. According to the initial result, optimized motor geometries provided an efficiency increase of about 3.54% and a cost reduction of about 17%. In addition, the search strategies of both algorithms were compared on motor design parameters.

Reduced-Pin-Count BOST for Test-Cost Reduction

Built-off self-test (BOST) is a widely used technique to reduce the test cost. It makes it possible to test high-speed dynamic random-access memory (DRAM) without using a costly high-performance automatic test equipment (ATE). However, the currently used BOSTs require many ATE connection pins, which degrade the cost reduction effect. In this article, we propose a novel reduced-pin-count BOST to reduce the test cost. The proposed BOST uses bidirectional pins to employ the pins as efficiently as possible. Thus, even if the same amount of data is transferred, fewer pins are required than the previous BOSTs. In addition, it reduces the amount of output data by sending only the information necessary for a DRAM repair process. This is possible because the DRAM repair process requires only the location information of some faulty cells. Therefore, the proposed BOST can send output data with fewer pins compared with the previous BOSTs. Experimental results indicate that the proposed BOST can test high-speed DRAMs using a few ATE connection pins.

Evaluation of a Two-Tier Adaptive Indirect Test Flow for a Front-End RF Circuit

This paper focuses on test cost reduction for RF circuits based on the use of an indirect test strategy. The implementation of a two-tier adaptive test flow is investigated, in which only circuits with a sufficient prediction confidence level are evaluated by the indirect test while others are re-evaluated by specification-based test. A methodology is presented that permits to explore different tradeoffs between test quality and test cost and to make pertinent choices for the efficient implementation of such a test flow. The methodology is applied to a front-end RF circuit designed for WLAN applications and results show that substantial test cost reduction can be achieved without compromising the test quality.