scholarly journals Using Swarm Intelligence to Generate Test Data for Covering Prime Paths

2017 ◽  
pp. 132-147 ◽  
Author(s):  
Atieh Monemi Bidgoli ◽  
Hassan Haghighi ◽  
Tahere Zohdi Nasab ◽  
Hamideh Sabouri
Keyword(s):  
Swarm Intelligence ◽  
Test Data ◽  
Generate Test Data

Cost and Effectiveness of Search-Based Techniques for Model-Based Testing: An Empirical Analysis

2017 ◽  
Vol 27 (04) ◽  
pp. 601-622 ◽  
Author(s):  
Aneesa Saeed ◽  
Siti Hafizah Ab Hamid ◽  
Asmiza Abdul Sani
Keyword(s):  
Test Data ◽  
Random Search ◽  
Test Cases ◽  
Optimal Test ◽  
Model Driven ◽  
Model Based ◽  
Model Based Testing ◽  
Generate Test Data ◽  
The Cost ◽  
Model Driven Approach

Model-based testing (MBT) seems to be gaining interest in industry and academia due to its provision of systematic, automated and comprehensive testing. The challenge in MBT is to generate optimal test data to execute test cases. Recently, researchers have successfully applied search-based techniques (SBTs) by automating the search for an optimal set of test data at reasonable cost compared to other more expensive techniques. In real complex systems, effectiveness and cost of SBTs for MBT in industrial context are little known. The objective of this study is to empirically evaluate the cost and the effectiveness of SBTs for MBT on industrial case studies. We applied a model-driven approach and SBTs to automatically generate executable feasible test cases. The results show that the model-driven approach generated high number of infeasible test cases with less time while genetic algorithm (GA) and simulating annealing (SA) outperformed significantly random search (RS) with high generation time. We concluded that local SBTs are more appropriate to generate test data when the type of the constraints is simple. Current work on analyzing the cost and effectiveness on SBTs for MBT indicates possible enhancement using the model-driven approach to detect the infeasible paths and SBTs to achieve optimal success rate.

scholarly journals PEMBANGKITAN DATA UJI BERBASIS DIAGRAM AKTIVITAS DAN DIAGRAM STATECHART

JOUTICA ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 137
Author(s):  
Arif Rahman Sujatmika ◽  
Yanuangga Gala Hartlambang
Keyword(s):  
Test Data ◽  
Test Case ◽  
Test Cases ◽  
Data Dictionary ◽  
Activity Diagram ◽  
Statechart Diagram ◽  
The Status ◽  
Generate Test Data ◽  
Case Data ◽  
Selection Of

Testing is the stage of software development used to determine whether a software is ready for release or not. In making test cases using reference activity diagrams and statechart diagrams, a help representation was made, ie State-Activity-Diagram (SAD). The generation of test cases using a reference between the statechart diagram and the status diagram is still inadequate because in the case of the test produced there is no test data. The selection of test data for many test cases will be tedious and time consuming. In this paper, it is proposed to generate test data automatically based on existing test cases. Test data created based on class diagrams, and data dictionaries. The test case data consists of inputs and results. First enter information about the functions involved in the test case into the SAD node so that the SAD-S Diagram is obtained. Second, after the process of making the test case is completed, the test data is made by looking at the data dictionary function so that the test data is formed.

Evolutionary Approaches to Test Data Generation for Object-Oriented Software

2018 ◽  
pp. 162-194
Author(s):  
Ana Filipa Nogueira ◽  
José Carlos Bregieiro Ribeiro ◽  
Francisco Fernández de Vega ◽  
Mário Alberto Zenha-Rela
Keyword(s):  
Test Data ◽  
Relevant Literature ◽  
Object Oriented ◽  
Data Generation ◽  
Open Problems ◽  
Branch Coverage ◽  
Evolutionary Testing ◽  
Generate Test Data ◽  
Tools And Techniques ◽  
Future Work

In object-oriented evolutionary testing, metaheuristics are employed to select or generate test data for object-oriented software. Techniques that analyse program structures are predominant among the panoply of studies available in current literature. For object-oriented evolutionary testing, the common objective is to reach some coverage criteria, usually in the form of statement or branch coverage. This chapter explores, reviews, and contextualizes relevant literature, tools, and techniques in this area, while identifying open problems and setting ground for future work.

Cardiac microimpedance measurement in two-dimensional models using multisite interstitial stimulation

2006 ◽  
Vol 290 (5) ◽  
pp. H1976-H1987 ◽  
Author(s):  
Andrew E. Pollard ◽  
Roger C. Barr
Keyword(s):  
Test Data ◽  
Ventricular Myocytes ◽  
Electrophysiological Study ◽  
Two Dimensional ◽  
Dynamic Membrane ◽  
Anisotropic Models ◽  
Wide Range ◽  
Dimensional Models ◽  
Generate Test Data ◽  
Interstitial Potential

We analyzed central interstitial potential differences during multisite stimulation to assess the feasibility of using those recordings to measure cardiac microimpedances in multidimensional preparations. Because interstitial current injected and removed using electrodes with different proximities allows modulation of the portion of current crossing the membrane, we hypothesized that multisite interstitial stimulation would give rise to central interstitial potential differences that depend on intracellular and interstitial microimpedances, allowing measurement of those microimpedances. Simulations of multisite stimulation with fine and wide spacing in two-dimensional models that included dynamic membrane equations for guinea pig ventricular myocytes were performed to generate test data (∂φo). Isotropic interstitial and intracellular microimpedances were prescribed for one set of simulations, and anisotropic microimpedances with unequal ratios (intracellular to interstitial) along and across fibers were prescribed for another set of simulations. Microimpedance measurements were then obtained by making statistical comparisons between ∂φo values and interstitial potential differences from passive bidomain simulations (Δφo) in which a wide range of possible microimpedances were considered. Possible microimpedances were selected at 25% increments. After demonstrating the effectiveness of the overall method with microimpedance measurements using one-dimensional test data, we showed microimpedance measurements within 25% of prescribed values in isotropic and anisotropic models. Our findings suggest that development of microfabricated devices to implement the procedure would facilitate routine measurement as a component of cardiac electrophysiological study.

Malware Behavior Analysis Technique Based on Approach to Sensitive Behavior Functions

2012 ◽  
Vol 263-266 ◽  
pp. 3034-3040
Author(s):  
Yan Feng Qin ◽  
Qing Xian Wang ◽  
Yong Jun Zeng ◽  
Qi Xi
Keyword(s):  
Behavior Analysis ◽  
Test Data ◽  
Symbolic Execution ◽  
Behavior Detection ◽  
Analysis Technique ◽  
Path Search ◽  
Function Identification ◽  
Behavior Function ◽  
Generate Test Data ◽  
Sensitive Behavior

Currently, the malware behavior analysis technique spends a lot of time to generate test data. To improve it, this paper proposes a method of malware behavior analysis based on approach to sensitive behavior function. And the techniques of sensitive behavior function identification, sensitive path search and approaching sensitive behavior function are discussed in this paper. This method detects and analyzes the malware behavior by combining the concrete execution and symbolic execution together. It shows that this method can improve the efficiency of malware behavior detection by comparing it with fuzz and full path traversing technique.

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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