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Articles by Rajender Singh Chhillar
Total Records ( 2 ) for Rajender Singh Chhillar
  Anju Bala and Rajender Singh Chhillar
  Selection of test case is a standard testing technique to opt a subset of existing test cases for execution, due to the limited budget and other necessary constraints. The key objective of this study is automatic generation and optimization of test cases using bio-inspired Genetic Algorithm (GA). These search optimization techniques lead to global best solution. These algorithms are used to generate test paths and then optimize them. The case study on telemedicine simulation system is being presented here using use case diagrams, activity diagram and sequence diagram. Activity diagram graph and sequence diagram graph show test paths which are being optimized using Genetic algorithm. This study presents a novel approach for generation of test cases using UML. Our approach consists of converting the all UML diagrams into graph and integrated to form System Under Test (SUT). From the graphs different control flow series also called test cases are recognized and then optimized using Genetic algorithm. The system graph is then traversed to generate test paths which are being optimized using GA. To explore the efficacy of our approach, we performed an empirical study using MATLAB programs with manifold paths and other parameters. Our results indicate that generation and optimization of test case is achieved efficiently in much less time.
  Sonal Gahlot and Rajender Singh Chhillar
  This study discusses a component based metric named as CI (Complexity of Interface), for the software complexity analysis at any stage of software development life cycle. The metric CI uses the coupling along with cohesion between different component of same or different modules, respectively. This metric also covers the anonymous classes and inner classes. The analysis of the metric is done by using the Non-dominated Sorting Genetic Algorithm 3 (NSGA-III) with discriminant analysis as the fitness function. The NSGA-III is the one of the latest and stable version modification of the Genetic algorithm used for optimization purposes. The analysis clearly shows that the CI metric computes the complexity of the interface effectively and can replace the existing coupling, cohesion metric metrics.
 
 
 
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