System-level test coverage prediction by structural stress test data mining

Treadmill exercise training in chronic obstructive pulmonary disease.

The steady march of Moore's Law in semiconductors has enabled the creation of ever more complex systems with electronics playing a central role. As a result, thorough testing of individual components is no longer adequate to ensure overall system performance, quality, and reliability. The rising importance of system-level test (SLT) to supplement traditional component structural test has gained wide recognition recently. To understand this trend, we examine where traditional testing falls short and where SLT fills the gap. In many cases, system failures are the result of complex component interactions leading to abnormal scenarios not attributable to simple and single root causes. Rather than being confined by traditional gate-level fault models, it might be more appropriate to develop a system-level fault model derived as an emergent property of complex systems. New definitions of fault coverage and how to automate high-coverage test generation are obvious challenges in the system domain. Recent developments in design verification which faces similar challenges in dealing with system complexity may offer possible solutions for SLT to draw on. The aim is to stimulate new ideas and directions for future research in system-based testing as upcoming 5G/IoT/AI-based applications penetrate deeper and pervasively into our daily lives.

Stress echocardiography has become an established tool for the diagnosis of coronary artery disease. However, in the current era of attention to cost effectiveness, the ability of a non-invasive test to predict outcomes, and therefore assist in clinical decision making, is paramount. As a reflection of the relative youth of this technique, there are fewer data using stress echocardiography to assess prognosis than there are using nuclear imaging. However, using both exercise and pharmacological stress echocardiography, the presence of ischaemia is strongly predictive of the recurrence of a subsequent cardiac event. Moreover, during pharmacological stress testing, the ischaemic threshold may be used to further stratify the risk of a positive test. More studies are needed in large populations to establish the prognostic role of stress echocardiography, as in independent predictor of outcome, incremental to clinical and stress testing data.

Automated test data generation is a challenging problem for researchers in the area of software testing. Up until now, most of the work on test data generation is at unit level. Until level test data generation involves the execution of test path at unit level where interaction with other components is minimum. Test data generation for unit testing involves a single path and there is no usage of formal and actual parameters. The problem of automated test data generation becomes very challenging when we move to other levels of testing including integration testing and system level testing. At integration level, the variables are passed as arguments to other components and variables change their names; also multiple paths are executed from different components to ensure proper functionality. Recently evolutionary approaches have been proven a powerful tool for test data generation. In this paper, we have proposed a novel approach for test data generation for coupling based integration testing using particle swarm optimization. Up until now, there is no research for test data generation for coupling based integration testing using particle swarm optimization. Our approach takes the coupling path as input, containing different sub paths, and generates the test data using particle swarm optimization. We have also proposed architecture of tool for automation of our approach. In future, we will implement our proposed approach and will perform different experiments to prove its significance.

Failure Mode and Effects Analysis (FMEA) is a key safety assessment analysis that determine failure modes at system, hardware and software level. Overlooking Failure Modes can often cause system or functionality failure which directly impacts a systems safety performance, reliability and quality. FMEA is a bottom-up approach which has four key phases - identification of fault, assessment of impact, determination of potential causes and their resolutions, and finally testing and documentation of analysis. FMEA addresses the effect of failures at the system, software and hardware level. The outcome of the analysis helps us identify gaps in safety requirements specification and provides input for component testing, integration testing and system level testing. This paper describes the application of Failure Mode and Effects Analysis (FMEA) to software modules.

Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization

Despite improvements in identifying high-risk patients with non-ST segment ACS (acute coronary syndrome), low risk patients presenting with atypical chest pain and non-diagnostic Electrocardiogram (ECG) continued to undergo unnecessary admissions and testing. Since 1992, our chest pain protocol included using 4-hour serial biomarkers from ED admission in combination with stress testing to evaluate these patients. Our study aimed at determining whether a new accelerated diagnostic protocol using sensitive cardiac troponin I (cTnI) 2 hours after admission to the ED followed by stress testing is safe and effective in emergency settings, allowing for appropriate triage, earlier discharge and reducing costs. We conducted a single center randomized trial at Presence St. Francis Hospital Chest pain center in Evanston, Illinois enrolling sixty-four consecutive patients with atypical chest pain and non-diagnostic ECG, participants were randomized to accelerated 2 hrs protocol or our pre-existing 4-hrs protocol. Sixty patients completed the protocol and were randomized to either a 2-hour (29 patients) or 4-hour protocol using both I-STAT and PATHFAST cTnI (31 Patients). Troponin I was evaluated at 0 and at 2 hours from ED presentation with and additional draw for patients in the 4-hour rule out-group. Patients with normal serial biomarkers were then evaluated with stress testing and qualified for earlier discharge if the stress test was negative, while those with a positive biomarker at any time were admitted. Thirty-six patients had exercise treadmill stress test and 24 patients had either nuclear or Echo stress test. Fifty-three patients had a normal stress test and were discharged home. One patient in the 4-hour group with normal serial troponins developed ventricular tachycardia/fibrillation during the recovery period of a regular stress test. Six patients had a positive PATHFAST cTnI and a normal I-STAT cTnI at 2-hours. Two out of these six patients evaluated by coronary angiography. One patient had severe tortuous coronaries but no significant obstructive lesion and one had a severe CAD who needed Coronary artery bypass grafting (CABG). Three of the six patients had a normal stress test and one patient decided to leave without further testing. None of the patients with a normal stress test had a major cardiac event or adverse cardiac outcome at six-month follow up. This study demonstrates that the 2 hours accelerated protocol using high sensitivity Troponin assay at 0 and 2 hours with comprehensive clinical evaluation and ECG followed by stress testing might be successful in identifying low-risk patient population who may benefit from early discharge from ED reducing associated costs and length of stay.

This chapter presents a Bayesian methodology for system-level uncertainty quantification and test resource allocation in complex engineering systems. The various component, subsystem, and system-level models, the corresponding parameters, and the model errors are connected efficiently using a Bayesian network. This provides a unified framework for uncertainty analysis where test data can be integrated along with computational models across the entire hierarchy of the overall engineering system. The Bayesian network is useful in two ways: (1) in a forward problem where the various sources of uncertainty are propagated through multiple levels of modeling to predict the overall uncertainty in the system-level response; and (2) in an inverse problem where the model parameters of multiple subsystems are calibrated simultaneously using test data. Test data available at multiple data are first used to infer model parameters, and then, this information is propagated through the Bayesian network to compute the overall uncertainty in the system-level prediction. Then, the Bayesian network is used for test resource allocation where an optimization-based procedure is used to identify tests that can effectively reduce the overall uncertainty in the system-level prediction. Finally, the overall Bayesian methodology for uncertainty quantification and test resource allocation is illustrated using three different numerical examples. While the first example is mathematical, the second and third examples deal with practical applications in the domain of structural mechanics.

The IMF Caribbean Regional Technical Assistance Centre (CARTAC) conducted a technical assistance mission to Barbados from July 31 to August 11, 2023. The mission aimed to strengthen the stress testing framework of the Central Bank of Barbados (CBB) and the Barbados Financial Services Commission (FSC) by enhancing their solvency stress testing (ST) capabilities for banks and credit unions. During the mission, the team worked closely with authorities to develop two customized stress testing tools, allowing for multi-factor and multi-period solvency assessments under various macroeconomic scenarios. These tools integrate explicit macroeconomic projections and newly developed credit risk satellite models, enabling a more sophisticated approach to evaluating key financial stability indicators, such as non-performing loans (NPLs), capital adequacy ratios, and profitability metrics. An illustrative stress test was conducted using recent financial data to demonstrate the tool’s application. The mission also focused on capacity building, providing hands-on training to technical staff at CBB and FSC to ensure the effective use and long-term sustainability of the stress testing framework. Discussions emphasized the importance of regular stress testing exercises to monitor systemic risks and enhance financial sector resilience. The mission recommended that CBB and FSC conduct semi-annual stress testing exercises, with results incorporated into the Financial Stability Report (FSR).

In recent years, with the rapid development of artificial intelligence and other related technologies, the traditional automotive industry has begun to integrate information technology in an all-round way. Due to the contributions of computer vision, deep learning, and sensitive sensors, autonomous driving systems (ADS) has now achieved great progress. But as we all know, the primary requirement for autonomous driving is absolute safety. However, technology innovation has brought great challenges to the testing of ADS, and due to the high cost of field testing, industrial companies rarely open relevant test data for research. This paper aims to study existing testing methods for ADS. Our study shows that there are few published works focusing on testing aspects of ADS. However, there is an obvious trend on the record of published works on testing ADS. Also, we can find that most reviewed works focus on setting up virtual test environment including generating, synthesizing, or reconstructing test input data. They either treat ADS as a whole to conduct (sub) system level testing or limit ADS into certain scenarios. From this, we believe that testing of ADS has just begun to attract researchers' interest; great effort should be paid before ADS becomes maturer.

Results of a program to investigate the reliability characteristics of unencapsulated low-cost terrestrial solar cells using accelerated stress testing are presented. A total of seven types of cells were investigated. Results of extended (~ 1 year) bias-temperature stress testing of four cell types were obtained. An additional three cell types, including cells fabricated using the advanced technologies of ribbon-grown silicon and polycrystalline silicon, were subjected to various combinations of bias, temperature cycling stress tests. The spectrum of tests used was based on previous years' work, but differs from that used earlier. An electrical measurement procedure capable of distinguishing small changes in cell electrical parameters was used. Significant degradation was shown by some cell types in some stress tests. Other combinations of cell types and stress tests resulted in no detectable cell degradation. Analysis of the origins of the differences in degradation is continuing. Second quadrant characteristics of some cell types were also investigated in order to establish the electrical behavior of cells which may be exposed to this stress condition in modules deployed in the field.

PHARMACOLOGIC STRESS ECHOCARDIOGRAPHY: Dobutamine and Arbutamine Stress Testing

In this paper, we illustrate the effectiveness of wafer-level enhanced voltage stress (EVS) along with low voltage sweep (LVS), I/sub DDQ/ and other parametric tests to screen early life failure defects. Our experiment shows temporary undetected defects after repeated exposure to certain applied voltages. We demonstrate a statistical methodology to screen die with suspected early life failure defects.

To generate efficient test data is one of the major problems during testing phase. This task is complex and very time consuming and researchers proposed different methods for generating the test data. This paper proposed a technique that uses the source code of the program, transforms it into Control Flow Graph (CFG), thereafter calculate the outdegree and then apply Genetic Algorithm over it to generate valuable test data during testing phase. The advantage of using the concept of outdegree in CFG is that it simplifies the technique of calculating the fitness function and reduces the overall testing time. The proposed technique not only simplify the overall task of applying Genetic Algorithm operators over test data but also reduces the complexity and testing time thereby increasing the efficiency of the technique.

To investigate reasons for the wide variation in formal studies of sensitivity and specificity indexes for the diagnostic efficacy of the graded exercise test for angiographically defined coronary disease, data were collected on 205 consecutive exercise tests at two hospital-based exercise laboratories. For calculations of sensitivity and specificity, stress test data are usually analyzed with many exclusions for ineligibility, with equivocal results omitted, and only in patients undergoing angiography. Consequently, only 3% of patients who received the tests in this survey would have been included in a typical formal study of diagnostic efficacy. In the same way that the visible tip of an iceberg misrepresents its extent and depth, the patients assembled in studies of diagnostic tests may be a highly selected group that misrepresents the intended population.