Evaluating the quality of test data under the influence of vigilance parameter in FLEXFIS

2012 ◽  
Author(s):  
S. Anandhavalli ◽  
S.K. Srivatsa
Keyword(s):  
Test Data

scholarly journals Hubungan Antara Kualitas Guru dengan Prestasi Belajar Siswa pada Mata Pelajaran Housekeeping

Media Wisata ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sri Larasati
Keyword(s):  
Student Achievement ◽  
Data Analysis ◽  
Teacher Quality ◽  
Test Data ◽  
Moment Analysis ◽  
Analysis Method ◽  
Improve Performance ◽  
Employment Practices ◽  
The Relationship

Teacher Quality is one factor that determines student achievement, the research to find out the relationship and contribution to the quality of teachers to student achievement in subjects Housekeeping. This Reseach is expected to expective to be useful for teachers to improve performance. To measure student achievement are used Pearson Product Moment analysis method. Of test data analysis can be seen that there is asignificant relationship with the teacher quality anatara student achiement, which toount (7.09423) is greater than ttable (2.021). Whereas the contribution of teacher quality on student achiement is the amount of KP 46.64% while the remaining 53.36% is determinedby other variables is one of the largest employment practices in the industry.

scholarly journals 2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)

2019 ◽  
Vol 11 (19) ◽  
pp. 2219 ◽  
Author(s):  
Fatemeh Alidoost ◽  
Hossein Arefi ◽  
Federico Tombari
Keyword(s):  
Test Data ◽  
3D Models ◽  
Parametric Models ◽  
Aerial Images ◽  
Aerial Image ◽  
Absolute Deviation ◽  
Multi Scale ◽  
Knowledge Based ◽  
Linear Elements

In this study, a deep learning (DL)-based approach is proposed for the detection and reconstruction of buildings from a single aerial image. The pre-required knowledge to reconstruct the 3D shapes of buildings, including the height data as well as the linear elements of individual roofs, is derived from the RGB image using an optimized multi-scale convolutional–deconvolutional network (MSCDN). The proposed network is composed of two feature extraction levels to first predict the coarse features, and then automatically refine them. The predicted features include the normalized digital surface models (nDSMs) and linear elements of roofs in three classes of eave, ridge, and hip lines. Then, the prismatic models of buildings are generated by analyzing the eave lines. The parametric models of individual roofs are also reconstructed using the predicted ridge and hip lines. The experiments show that, even in the presence of noises in height values, the proposed method performs well on 3D reconstruction of buildings with different shapes and complexities. The average root mean square error (RMSE) and normalized median absolute deviation (NMAD) metrics are about 3.43 m and 1.13 m, respectively for the predicted nDSM. Moreover, the quality of the extracted linear elements is about 91.31% and 83.69% for the Potsdam and Zeebrugge test data, respectively. Unlike the state-of-the-art methods, the proposed approach does not need any additional or auxiliary data and employs a single image to reconstruct the 3D models of buildings with the competitive precision of about 1.2 m and 0.8 m for the horizontal and vertical RMSEs over the Potsdam data and about 3.9 m and 2.4 m over the Zeebrugge test data.

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

Methods of generating test data for carrying out the fuzzing process

2019 ◽  
Vol 0 (9/2019) ◽  
pp. 27-32
Author(s):  
Marcin Pachnik
Keyword(s):  
Test Data ◽  
Web Applications ◽  
Software Security ◽  
Local Network ◽  
Security Testing ◽  
Fuzz Testing ◽  
Modern Methods ◽  
Automatic Software ◽  
The Impact

The article presents and compares modern methods of generating test data in the process of automatic software security testing, so called fuzz testing. The publication contains descriptions of methods used, among others, in local, network or web applications, and then compares them and evaluates their effectiveness in the process of ensuring software security. The impact of the quality of test data corpus on the effectiveness of automated security testing has been assessed.

Curved-Wide Plate Testing of X100 Girth Welds

2014 ◽  
Author(s):  
Timothy S. Weeks ◽  
J. David McColskey ◽  
Mark D. Richards ◽  
Yong-Yi Wang ◽  
Marie Quintana
Keyword(s):  
Test Data ◽  
Tensile Strain ◽  
Considerable Effort ◽  
Future Paper ◽  
Model Predictions ◽  
Post Test ◽  
Consensus Standard ◽  
Girth Welds ◽  
Welded Pipe

Curved-wide plate (CWP) tests are frequently used for assessing the quality of pipeline girth welds. Despite a large number of CWP tests having been conducted at great expense over many decades, an industry consensus standard remains unavailable. Considerable effort at several research institutions is focused on the standardization of test protocols. It is widely recognized that comparing results from CWP tests from different institutions is difficult without accounting for all the possible parametric differences. This paper presents the procedural details recently used in testing X100 girth welds. The protocols cover (1) specimen design and dimensions, (2) instrumentation plan and data acquisition, (3) specimen fabrication and preparation, (4) preparing and executing the tests, (5) processing of raw test data and (6) post-test metallurgical examination. The evaluation of specimen deformation, flaw growth, and comparison of test data with model predictions will be presented in a future paper. Selected CWP test data from this program were evaluated and compared to tensile strain models of the girth welded pipe in a recent paper [1].

scholarly journals Analysis Model of the Impact of Refined Intervention in Operating Room on Patients’ Recovery Quality and Complications after Thoracic Surgery Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jun He ◽  
Jing Wen
Keyword(s):  
Neural Network ◽  
Thoracic Surgery ◽  
Operating Room ◽  
Test Data ◽  
Deep Neural Network ◽  
Quality Of Recovery ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Room Nursing

To improve the nursing effect in patients after thoracic surgery, this paper proposes a refined intervention method in the operating room based on traditional operating room nursing and applies this method to the nursing of patients after thoracic surgery. Moreover, this paper improves the traditional neural network algorithm and uses the deep neural network algorithm to process test data. In addition, it includes patients accepted by the hospital as samples for test analysis and formulates detailed intervention methods for the operating room. Finally, this paper collects the corresponding test data by setting up test and control groups and visually displays the data using mathematical statistics. The statistical parameters of the experiment in this paper include the quality of recovery, complications, satisfaction score, and recovery effect. The comparative test shows that the refined intervention in the operating room based on the neural network proposed in this paper can achieve a certain effect in the postoperative nursing of thoracic surgery, effectively promote the quality of recovery, and reduce the possibility of complications.

scholarly journals Wireless LAN Service Quality Optimization in Academic Environments

2017 ◽  
Vol 11 (10) ◽  
pp. 166
Author(s):  
Moses E. Ekpenyong ◽  
Uduak A. Umoh ◽  
Udoinyang G. Inyang ◽  
Aniekpeno M. Jackson
Keyword(s):  
Service Quality ◽  
Test Data ◽  
Wireless Lan ◽  
Accurate Estimation ◽  
Self Organizing Map ◽  
Quality Of Data ◽  
Inference System ◽  
Perceived Performance ◽  
Expected Performance

This paper targets optimized service quality (SQ) – a metric that compares the perceived performance by users with the expected performance – sufficient to satisfy users’ quality of experience (QoE). The perceived performance was obtained in a field survey from an academic environment, and using Interval Type-2 Fuzzy Logic (IT2FL), uncertainties inherent in the field data were efficiently modeled for accurate estimation of the SQ. To obtain the expected performance, two unsupervised tools: the Principal Component Analysis (PCA) and Self-organizing Map (SOM) were exploited to abstract the most relevant features, and observe similarity patterns between the abstract features. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was then used to optimize the system performance. Results obtained showed that ANFIS sufficiently optimized and modeled the SQ – as the root mean square error (RMSE) values of the train and test data were approximately the same – for all the study sites considered. However, combining the three campuses produced the least mean absolute error (MAE) of 0.0979 for train data, and the highest MAE of 0.7345 for test data. Further, the least MAE of 0.4707 for test data was obtained from town campus Annex. The wide variation in MAE observed in the train and test data might not be unconnected with the high degree of uncertainties associated with interference, site topology and terrain issues – exhibited by the system under study, as well as the quality of data collected. The proposed system framework has the potentials to develop into a complete location-based system.

scholarly journals Mutated Specification-Based Test Data Generation with a Genetic Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 331
Author(s):  
Rong Wang ◽  
Yuji Sato ◽  
Shaoying Liu
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Cases ◽  
Formal Specifications ◽  
Data Generation ◽  
Testing Methods ◽  
Functional Changes ◽  
Target Program ◽  
Generate Test Data

Specification-based testing methods generate test data without the knowledge of the structure of the program. However, the quality of these test data are not well ensured to detect bugs when non-functional changes are introduced to the program. To generate test data effectively, we propose a new method that combines formal specifications with the genetic algorithm (GA). In this method, formal specifications are reformed by GA in order to be used to generate input values that can kill as many mutants of the target program as possible. Two classic examples are presented to demonstrate how the method works. The result shows that the proposed method can help effectively generate test cases to kill the program mutants, which contributes to the further maintenance of software.

scholarly journals ICPFuzzer: proprietary communication protocol fuzzing by using machine learning and feedback strategies

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Pei-Yi Lin ◽  
Chia-Wei Tien ◽  
Ting-Chun Huang ◽  
Chin-Wei Tien
Keyword(s):  
Test Data ◽  
Short Term Memory ◽  
Security Threats ◽  
Short Term ◽  
Industrial Control ◽  
Feedback Strategies ◽  
Long Short Term Memory ◽  
Generate Test Data ◽  
Security Standards

AbstractThe fuzzing test is able to discover various vulnerabilities and has more chances to hit the zero-day targets. And ICS(Industrial control system) is currently facing huge security threats and requires security standards, like ISO 62443, to ensure the quality of the device. However, some industrial proprietary communication protocols can be customized and have complicated structures, the fuzzing system cannot quickly generate test data that adapt to various protocols. It also struggles to define the mutation field without having prior knowledge of the protocols. Therefore, we propose a fuzzing system named ICPFuzzer that uses LSTM(Long short-term memory) to learn the features of a protocol and generates mutated test data automatically. We also use the responses of testing and adjust the weight strategies to further test the device under testing (DUT) to find more data that cause unusual connection status. We verified the effectiveness of the approach by comparing with the open-source and commercial fuzzers. Furthermore, in a real case, we experimented with the DLMS/COSEM for a smart meter and found that the test data can cause a unusual response. In summary, ICPFuzzer is a black-box fuzzing system that can automatically execute the testing process and reveal vulnerabilities that interrupt and crash industrial control communication. Not only improves the quality of ICS but also improves safety.

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