Optimal Test Set Research Articles

Construction of hyperspectral reflectance and spectral index inversion model for the water content of Catalpa bungei leaves

Real-time monitoring of leaf water content is an important indicator of drought resistance in plants. In this study, hyperspectral reflectance and derived data are used to build an inversion model for leaf water content of Catalpa bungei. Rapid, non-destructive and real-time monitoring of leaf water content provides a high-throughput method for assessing drought resistance in tree seedlings. The hyperspectral reflectance and water content of mature leaves were determined and several models were built to evaluate the optimal combination using different variable selection and model construction methods. The results show that the PLS regression model constructed with reflectance as the input variable is the best for the test series. The MC-UVE method is the best for all models. With the PLS regression method, the model approach is optimal. MC-UVE-PLS model optimal test set regression coefficient (R2) maximum (0.7903), mean square root error (RMSE) minimum (1.7352). SR (1466 nm, 2128 nm) is the spectral index with the highest water correlation. First order differencing can effectively improve the correlation between the spectral data and water content, but the model cannot be optimised. Using MC-UVE as a variable screening method, PLS regression was used to build an inversion model for leaf water content, which provides technical support for real-time monitoring of leaf water content of Catalpa bungei.

Facial expression recognition for a seven-class small and medium-sized dataset based on transfer learning CNNs

As one of the most common biological information for human to express their emotions, facial expression plays an important role in biological research, psychological analysis and even human-computer interaction in the computer field. It is very important to use the efficient computing and processing power of computers to realize automatic facial expression recognition. However, in the research process of this field, most people's technology and model achievements are based on large datasets, which lack the universality of small and medium-sized datasets. Therefore, this project provides an optimization model on a specific seven-class small and medium face image dataset and provides a possible technical optimization reference direction for facial expression recognition models on similar small and medium-sized datasets. During the experiment, the training performance of VGG16 and MobileNet is compared. A comparative experiment is set up to observe the effect of transfer learning mechanism on training results. The results show that transfer learning has a significant effect on the performance of the model, and the accuracy of the optimal test set is more than 90%. Regardless of whether transfer learning mechanism is used, the training performance of VGG16 model structure is better than that of MobileNet structure in the same dataset.

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database.

Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820-0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/). The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.

The Control Relationship Between the Enterprise’s Electrical Equipment and Mechanical Equipment Based on Graph Theory

Abstract This article proposes a fault component location method based on graph theory and analyzes the properties of the elements in the fault judgment matrix. We can use the exclusive OR algorithm to determine the faulty section. On this basis, the realization model and algorithm essence of the optimal test set is given. At the same time, we propose an optimal test set design for circuit fault diagnosis based on graph theory. Finally, the realization of the article verified that the method adapts to the flexible operation mode of the active distribution network structure and can accurately determine the fault section.

Fault Detection of Wind Turbine Pitch System Based on Multiclass Optimal Margin Distribution Machine

In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate.

A Hybrid Artificial Bee Colony Strategy for t-way Test Set Generation with Constraints Support

t-way interaction testing is a systematic approach for exhaustive test set generation. It is a vital test planning method in software testing, which generates test sets based on interaction between parameters to cover every possible test sets combinations. t-way strategy clarifies the interaction strength between the number of parameters. However, there are some test sets combinations that should be excluded when generating the final test set as a result of invalid outputs, impossible or unwanted test sets combinations (e.g. system requirements set). These types of set combinations are known as constraint’s combinations or forbidden combinations. From existing studies, several t-way strategies have been proposed to address the test set combination problem, however, generating the optimal test set is still open research being an NP-hard problem. Therefore, this study proposed a novel hybrid artificial bee colony (HABC) t-way test set generation strategy with constraints support. The proposed approach is based on a hybrid artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. PSO was integrated as the exploratory agent for the ABC hence the hybrid nature. The information sharing ability of PSO via the Weight Factor is used to enhance the performance of ABC. The output of the hybrid ABC is a set of promising optimal test set combinations. The results of the experiments showed that HABC outperformed and yielded better test sets than existing methods (HSS, LAHC, SA_SAT, PICT, TestCover, mATEG_SAT).

Boolean Difference Technique for Detecting All Missing Gate and Stuck-at Faults in Reversible Circuits

Quantum reversible circuit is a new emerging technology attracting the researchers. A reversible circuit is composed of reversible gates. One example of reversible gate is Toffoli gate. A Toffoli gate (also known as [Formula: see text]-CNOT) has two components — the control and the target. Initially, stuck-at fault and other fault models were used for modeling defects in quantum reversible circuits. Later, a new fault model known as missing gate fault model was introduced, which is more effective in capturing the errors in quantum reversible circuit. Boolean Difference is already a known technique to detect stuck-at faults in conventional CMOS circuit. In this paper, Boolean Difference method is applied to derive the test set for detecting each stuck-at fault and missing gate fault in a reversible circuit. Then an optimization algorithm is used to derive an optimal test set, which will detect all possible faults in a circuit. The method is valid also for other fault models.

GAPSO: Optimal Test Set Generator for Pairwise Testing

Exhaustive testing is impossible for all sorts of software systems, owing to the cost and time consumption. Combinatorial testing is the solution to this issue and aims at picking the necessary set of parameters which can ensure high degree of interaction between the parameters. This paper presents a new approach for generating unique test cases by exploiting Genetic and Particle Swarm Optimization (GAPSO) algorithm for achieving pairwise testing. The generated test cases are refined, so as to arrive at the optimal test set. The outcome of the proposed algorithm is the minimal count of high quality test cases.

Employment of multiple algorithms for optimal path-based test selection strategy

Executing various sequences of system functions in a system under test represents one of the primary techniques in software testing. The natural way to create effective, consistent and efficient test sequences is to model the system under test and employ an algorithm to generate the tests that satisfy a defined test coverage criterion. Several criteria of test set optimality can be defined. In addition, to optimize the test set from an economic viewpoint, the priorities of the various parts of the system model under test must be defined. Using this prioritization, the test cases exercise the high priority parts of the system under test more intensely than those with low priority. Evidence from the literature and our observations confirm that finding a universal algorithm that produces an optimal test set for all test coverage and test set optimality criteria is a challenging task. Moreover, for different individual problem instances, different algorithms provide optimal results. In this paper, we present a path-based strategy to perform optimal test selection. The strategy first employs a set of current algorithms to generate test sets; then, it assesses the optimality of each test set by the selected criteria, and finally, chooses the optimal test set. The experimental results confirm the validity and usefulness of this strategy. For individual instances of 50 system under test models, different algorithms provided optimal results; these results varied by the required test coverage level, the size of the priority parts of the model, and the selected test set optimality criteria.

Optimal training and test sets design for machine learning

In this paper, we describe histogram matching, a metric for measuring the distance of two datasets with exactly the same features, and embed it into a mixed integer programming formulation to partition a dataset into fixed size training and test subsets. The partition is done such that the pairwise distances between the dataset and the subsets are minimized with respect to histogram matching. We then conduct a numerical study using a well-known machine learning dataset. We demonstrate that the training set constructed with our approach provides feature distributions almost the same as the whole dataset, whereas training sets constructed via random sampling end up with significant differences. We also show that our method introduces neither positive nor negative bias in prediction accuracy of a decision tree—used as a representative example of a machine learning method.

Digital NBC Protection System BIT Optimization Design

BIT technology is an important means of system and equipment fault detection, location and isolation. This paper takes the digital NBC protection system as the research object, and carries out research on the optimization design based on BIT technology. Based on the structural division of the digital NBC protection systems and the analysis of typical failure modes, combined with the weighted cost index function and the selection method of the improved special hierarchically optimized optimal test set, the calculation and comparison of the indices of the actual digitalized NBC protection systems was conducted. Determine the optimal test set, and finally design the BIT circuit for the selected optimal test set.

Research on Optimization of BIT Design Scheme for Digital NBC Protection System

BIT technology is an important means to detect, locate and isolate faults of system and equipment. In this paper, the digital NBC protection system is taken as the research object, and the on-line state detection method based on BIT technology is studied. The system structure division and analysis of typical fault mode have been done. The design flow of online state monitoring based on BIT technology is presented. A weighted cost indicator function is constructed. This paper also presents a selection method for selecting the optimal test set based on improved specially-stepwise optimal selection method for digital NBC protection system. From the theoretical point of view, the system typical fault mode is extracted and the BIT design objective is set, and finally, the optimal BIT design scheme is determined.

An accelerating PSO algorithm based test data generator for data-flow dependencies using dominance concepts

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like GA, PSO have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an accelerating particle swarm optimization algorithm (APSO) is applied to generate test data for data-flow dependencies of a program guided by a new fitness function. APSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology ERP system were taken for the experimental analysis. The experimental results show that the proposed APSO based approach performed significantly better than random search, genetic algorithm and PSO in enhancing the convergence speed.

An Adaptive PSO Algorithm Based Test Data Generator for Data-Flow Dependencies using Dominance Concepts

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like genetic algorithm (GA), Particle Swarm Optimization (PSO) have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an Adaptive Particle Swarm Optimization (APSO) algorithm is applied to generate test data for data-flow dependencies of a program guided by a novel fitness function. Adaptive PSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology (KIET), Enterprise resource planning (ERP) system were taken for the experimental analysis. The experimental results show that the proposed adaptive PSO based approach performed significantly better than random search, Genetic Algorithm and PSO in enhancing the convergence speed.

Artificial Bee Colony based Test Data Generation for Data-Flow Testing

Objectives: It is a challenging task to generate and identify an optimal test set that satisfies a robust adequacy criterion, like data flow testing. A number of heuristic and meta-heuristics algorithms like GA, PSO have been applied to optimize the Test Data Generation (TDG) problem. The aim of this research work is to handle the automatic Test Data Generation problem. Methods/Statistical Analysis: This research work focuses on the application of Artificial Bee Colony (ABC) algorithm guided by a novel Fitness Function (FF) for TDG problem. The construction of FF based on the concept of dominance relations, weighted branch distance for ABC to guide the search direction. Ten well known academic programs were taken for experimental analysis. The proposed algorithm is implemented in C environment. Findings: To examine the effectiveness of ABC algorithm in Test Data Generation, ten academic programs were taken experiment. The effectiveness of proposed algorithm is evaluated using average number of generations and coverage percentages achieve parameters. The experimental results show that proposed ABC algorithm requires less number of generations in comparison to other algorithms. It is also noted that the proposed algorithm coverage almost all def-path for all programs. Application/Improvements: The experimental results depict that the ABC algorithm performs far better than other existing algorithm for optimizing test data.

Using Presilicon Knowledge to Excite Nonlinear Failure Modes in Large Mixed-Signal Circuits

With the continuous growing of analog circuit complexity, identifying critical failure modes is of great importance today. This article develops an information-theoretic framework to rank important parameters based on presilicon data. The proposed framework is further used to design the optimal test set that maximizes the probability of observing out-of-specification failures. A phase-locked loop example is adopted to validate the proposed framework.

An Exact approach for Complete Test Set Generation of Toffoli-Fredkin-Peres based Reversible Circuits

Reversible logic has gained interest of researchers worldwide for its ultra-low power and high speed computing abilities in the future quantum information processing. Testing of these circuits is important for ensuring high reliability of their operation. In this work, we propose an ATPG algorithm for reversible circuits using an exact approach to generate CTS (Complete Test Set) which can detect single stuck-at faults, multiple stuck-at faults, repeated gate fault, partial and complete missing gate faults which are very useful logical fault models for reversible logic to model any physical defect. Proposed algorithm can be used to test a reversible circuit designed with k-CNOT, Peres and Fredkin gates. Through extensive experiments, we have validated our proposed algorithm for several benchmark circuits and other circuits with family of reversible gates. This algorithm produces a minimal and complete test set while reducing test generation time as compared to existing state-of-the-art algorithms. A testing tool is developed satisfying the purpose of generating all possible CTS's indicating the simulation time, number of levels and gates in the circuit. This paper also contributes to the detection and removal of redundant faults for optimal test set generation.

Construction of Mixed Covering Arrays for Pair-wise Testing Using Probabilistic Approach in Genetic Algorithm

In a system with large number of input parameters, it is necessary to check for errors that can occur as a result of interactions between various input parameters. However, checking of all possible combinations of input parameters is often restricted due to time and budget constraints. In order to overcome the constraints of exhaustive testing, combinatorial testing has been employed to generate optimal and efficient test set that covers all t-way combinations of input parameters. Pair-wise testing, a combinatorial testing technique, tests all possible combinations of each pair of input parameter values. In this paper, we present an efficient algorithm pair-wise test set generator using genetic algorithm (PTSG-GA) for generating test set for pair-wise testing. PTSG-GA is an extension of our previous work that applies genetic algorithm (GA) to generate optimal test set for pair-wise testing. In this paper, combinatorial objects, namely covering array (CA) and mixed covering arrays (MCA), are used to represent test set. The major contribution of algorithm PTSG-GA is that it uses a probabilistic approach to generate initial population of CAs/MCAs to improve the performance of GA. The algorithm PTSG-GA is implemented using an open-source tool PWiseGen. We have reported experimental results that illustrate the effectiveness of PTSG-GA as compared to existing state-of-the-art algorithms.

Comparative Analysis of Determinant Elimination Method, Ant Colony Algorithms and Genetic Algorithm

In this paper, the analysis of optimizing test sets of which the optimal solutions are already known is made first. Then the optimization results and execution time of Determinant Elimination Method, Ant Colony Algorithms as well as Genetic Algorithm are compared. At last, Based on the concept of optimal test set proposed in this paper, plenty of test sets which need to be optimized are randomly generated. The optimization algorithm proposed in this paper is also used to optimize the test sets and the consequences of optimization is desirable.

Going beyond “same-for-all” testing of infectious agents in donated blood

Blood products, derived from donated blood, are essential for many medical treatments, and their safety, in terms of being free of Transfusion-Transmitted Infections (TTIs)—i.e., infectious agents that can be spread through their use—is crucial. However, blood screening tests are not perfectly reliable and may produce false negative or false-positive results. Currently, blood donations are tested using a same-for-all testing scheme, where a single test set is used on all blood donations. This article studies differential testing schemes, which may involve multiple test sets, each applied to a randomly selected fraction of the donated blood. Thus, although each blood donation is still tested by a single test set, multiple test sets may be used by the Blood Center. This problem is modeled within an optimization framework and a novel solution methodology is provided that allows important structural properties of such testing schemes to be characterized. It is shown that an optimal differential testing scheme consists of at most two test sets, and such a dual-test scheme can significantly reduce the TTI risk over the current same-for-all testing. The presented analysis leads to an efficient greedy algorithm that generates the optimal differential test sets for a range of budgets to inform the decision-maker (e.g., Blood Center). The differential model is extended to the case where different test sets can be used on sub-sets of donations defined by donation characteristics (e.g., donor demographics, seasonality, or region) that differentiate the sub-set’s TTI prevalence rates. The risk reduction potential of differential testing is quantified through two case studies that use published data from Sub-Saharan Africa and the United States. The study generates key insight into public policy decision making on the design of blood screening schemes.