Genetic Algorithms in Test Design Automation

2014 ◽  
Vol 693 ◽  
pp. 153-158 ◽  
Author(s):  
Michal Sroka ◽  
Roman Nagy ◽  
Dominik Fisch
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Design Automation ◽  
State Of The Art ◽  
Embedded Software ◽  
Test Design ◽  
Test Case ◽  
Current State ◽  
Development Costs ◽  
Case Design

Automation in the software testing process has significant impact on the overall software development in industry. Therefore, any automation in software testing has huge influence on overall development costs. The present article reviews the current state of the art of test case design automation via genetic algorithms. Three approaches applied in software testing are described with regards to their applicability in the testing of embedded software.

scholarly journals Testing Complexity in Component based Software Engineering

2019 ◽  
Vol 8 (11) ◽  
pp. 656-660
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Time Budget ◽  
Test Case ◽  
Testing Strategy ◽  
Road Map ◽  
Software Product ◽  
Component Based Software Engineering ◽  
The Right ◽  
Case Design

The Component Based software development which reduces the time, budget and efforts compare to the traditional software development. The components are reusable, developed by third parties and available at Commercial-Of-The Self (COTS). Most recent technologies such embedded systems, mobile phones, control systems and other paradigms are developed with this concept. Software researchers should pay more attention on testing of Component Based technology. The software testing is the most commonly used technique for validating the product quality. It is something different from the traditional software development, due to the invisible source code and design make it very difficult to trace the faults. Software testing strategy is road map integrates test case design methods into well planned series of steps for successful construction of software. The selection right strategy at the right time will make the software product as robust. This paper highlights the various software testing challenges, difficulties, strategies and techniques in perspective of Component Based Software Development

Embedded Software Testing Requirements Modeling and Automatic Test Case Generation Based on Multiple Graphs

2015 ◽  
Vol 21 (11) ◽  
pp. 3530-3535
Author(s):  
Ming-Cheng Qu ◽  
Nai-Gang Cui ◽  
Ya-Nan Zhang ◽  
Xiang-Hu Wu ◽  
Bing-Song Zou
Keyword(s):  
Software Testing ◽  
Embedded Software ◽  
Test Case Generation ◽  
Test Case ◽  
Automatic Test ◽  
Automatic Test Case Generation ◽  
Requirements Modeling

Embedded Software Formalization Testing Method and its Application in Missile-Borne Software

2014 ◽  
Vol 574 ◽  
pp. 646-650
Author(s):  
Ji Cun Ma ◽  
Xing Lin Qi ◽  
Jing Lv
Keyword(s):  
Software Testing ◽  
Formal Method ◽  
Unified Modeling Language ◽  
Embedded Software ◽  
Test Case ◽  
Unified Modeling ◽  
Testing Method ◽  
Finite State ◽  
Z Language ◽  
Collection Methods

The formal modeling method can accurately describe systems. Using formal method to test software improves the efficiency of software testing and it is comprehensive. Common real-time embedded software formal testing methods include Z language, finite state machine (FSM) and unified modeling language (UML), Petri net and time input/output automata (TIOA). This paper analyzes the characteristics of embedded software testing. The above formal methods and test case coverage criterions and test data collection methods are introduced. Combined with the characteristics of missile-borne software, this paper presents suitable ideas of testing missile-borne software.

scholarly journals A Novel Framework Design for Test Case Authoring and Auto Test Scripts Generation

2021 ◽  
Vol 12 (6) ◽  
pp. 1479-1487
Author(s):  
Srinivas Perala, Dr. Ajay Roy
Keyword(s):  
Software Testing ◽  
Embedded Software ◽  
Product Launch ◽  
Software Test ◽  
Design For Test ◽  
Test Case ◽  
Test Cases ◽  
Test Automation ◽  
Automation Test ◽  
Automate Testing

Every product has defects and identifying defects in the process of development and rectifying them before the launch of the product is very important. Embedded software testing process find the bugs in the software and report to the developer to fix issues. Sometimes to meet the product release deadlines, test engineers will not get much time to cover all test cases. That is why most software testing depends on test automation. In this paper, we focused on the area of automotive and home appliances embedded software test automation. Test automation is the only solution to improve the test phase and meet the timeline of the product launch. There are many test Automation tools like LabVIEW, test stand, and automation desk to automate testing embedded software. However, there is still manual efforts are required to use these tools. This paper deals automate those manual efforts. This Works shows how to generate test scripts from test cases to reduce the manual efforts, time, and cost.

scholarly journals Software Testing for Machine Learning

2020 ◽  
Vol 34 (09) ◽  
pp. 13576-13582
Author(s):  
Dusica Marijan ◽  
Arnaud Gotlieb
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Research Agenda ◽  
Software Verification ◽  
State Of The Art ◽  
Learning Systems ◽  
Safety Critical ◽  
Current State ◽  
Established Technique ◽  
Summary Talk

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machine learning, especially in safety-critical applications, unless we are able to assure its correctness and trustworthiness properties. Software verification and testing are established technique for assuring such properties, for example by detecting errors. However, software testing challenges for machine learning are vast and profuse - yet critical to address. This summary talk discusses the current state-of-the-art of software testing for machine learning. More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations. The paper provides a research agenda with elaborated directions for making progress toward advancing the state-of-the-art on testing of machine learning.

An Approach to Generate Test Cases by Multi-Path Based on Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3976-3979
Author(s):  
Yu Liu ◽  
Feng Qin Wang ◽  
Xiu Li Zhao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Software Testing ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Test Environment ◽  
Quality And Reliability ◽  
Classification Procedures

Software testing is important to ensure the quality and reliability of the software.The improvement on the automation of test case generation is the entire key to improve the automation of the testing process.It helps a lot in the generation of test cases to construct multi-path model.It is based on genetic algorithm with three parts which are the test environment construction, the genetic algorithms and the operating environment.It’s feasibility and efficiency is verified by triangle classification procedures.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

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