model driven testing
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The method mixes up the extended finite state machine & UML activity diagram to generate the test model. H good coverage of test of all probable scenarios. Here an activity diagram describes the operation of the system, decision ere we have considered different coverage criteria for generating the test paths from the model for node transition from one action state to another. Also flow of control is represented. These will emphasis on sequence and condition of flow. It also gives idea about internal nodes. Refactoring is the process of altering an application’s source code of its external behavior is not changing. The purpose of code refactoring is to improve some of the nonfunctional properties of the code, such as readability, complexity, maintainability and extensibility. Refactoring can extend the life of source code, preventing it from becoming legacy code. The refactoring process makes future enhancements to such code a more pleasant experience. Refactoring is also known as reengineering. Test cases tend to be massive in range as redundant take a look at cases square measure generated because of the presence of code smells, thus the requirement to scale back these smells. Methods Statistical Analysis: This analysis adopts a proactive approach of reducing action at laws by police investigation the lazy category code smells supported the cohesion and dependency of the code and applying the inline category refactoring practices before take a look at case generation there by considerably avoiding redundant take a look at cases from being generated..


2019 ◽  
Vol 27 (2) ◽  
pp. 501-527
Author(s):  
Omer Nguena Timo ◽  
Alexandre Petrenko ◽  
S. Ramesh

Author(s):  
Rachita Misra ◽  
Chhabi Rani Panigrahi ◽  
Bijayalaxmi Panda ◽  
Bibudhendu Pati

This chapter on “Software Design” emphasizes the role of modeling, prototyping, and simulation in software design. The chapter introduces the principles of software design, issues and challenges. Modeling techniques used in procedural and object oriented methodologies is presented along with the Unified Modeling Language (UML). The suitability of prototyping, as a design artifact and a simulation method is briefly discussed. Software processes such as Rapid Application Development (RAD), Rational Unified Process (RUP) and Agile methodologies which influence the design process have been discussed and recommended. The chapter then deals with Design Metrics for Quality Analysis, Software Risk Analysis and Threat Modeling for design of secure software. Finally, some of the recent research topics such as Model Driven Architecture (MDA), Model Driven Development (MDD), Meta Object Facility (MOF), and Model Driven Testing (MDT) have been covered.


2017 ◽  
Vol 9 (4) ◽  
pp. 90
Author(s):  
Romulo De Almeida Neves ◽  
Willian Massami Watanabe

Contexto: Model Driven Development (MDD) eleva a importância de modelos dentro do ciclo de vida do software, incorporando-os como parte integrante do produto final por meio de técnicas de modelagem e geração de código, com isso, parte da complexidade do software fica escondida dentro de geradores. Problema: Mesmo com a utilização do processo MDD, o custo associado à elaboração de casos de teste ainda é alto. Objetivo: Automatizar a geração de casos de teste incorporando o Model Driven Testing (MDT) em um processo MDD já existente a fim de gerar automaticamente casos de teste de aceitação para um sistema legado. Justificativa: Garantir a qualidade do software gerado através de um processo de geração de código e reduzir o tempo geral do ciclo de vida do software. Método: Foi realizado uma prova de conceito como forma de validar o objetivo deste trabalho e avaliar a efetividade da abordagem de teste utilizando a métrica de Code Coverage dos casos de testes gerados automaticamente. Resultados: Para este estudo foram selecionados cinco Graphical User Interface (GUIs), na qual a GUI Seguradoras obteve 98% de cobertura de código pelos casos de teste, Moeda obteve 94,39%, Fornecedor com 95,04%, Tipo do item com 95,95% e por fim a GUI Classificação Contábil obteve 95,91% de cobertura dos casos de teste.


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