<span>Being a crucial step and deciding factor for software reliability, software testing has evolved a long way and always attracted researchers due to various inherent challenges. The quality of a software application depends on the effectiveness of the testing carried out during development and maintenance phase. Testing is a crucial but time consuming activity that influences the overall cost of software development. Thus a minimal but efficient test suite selection is the need of the hour. This paper presents a hybrid technique based on swarm based search technique and GA (Genetic Algorithm) for selection of promising test cases to reduce the overall development cost and time of the application. We took component based software into consideration as they offer some inherent advantages over traditional software development paradigms.</span>
Reusability is one of the prime aspects of high quality software. Based on the concept of reusing the previous effort, Component based software engineering is a widely evolving software development paradigm that sets new challenges for testing team. The third party components need to be selected and assembled in development framework. Components interact with each other for various services and the interface between them can prove as the point of failure. As exhaustive testing of all interaction sequences is not possible, there is need for automated test case reduction and prioritization techniques to increase the efficiency of testing process. Ant Colony Optimization (ACO), a nature inspired optimization technique has wide range of applications in the field of software engineering. This paper presents an ACO based technique for test case selection for interaction testing of reusable software components.
Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.
The development of high quality software is the need of current technology driven world. Component Based Software Engineering (CBSE) has provided a cost effective, fast and modular approach for developing complex software. CBSE is mainly based on the concept of reusability. Apart from these CBSE has several advantages as well as challenges which are summarized in this paper. Large and complex software development requires management of reusable components and can be selected from component repository and assembled to obtain a working application. Development of components and their assembly is different from traditional softwares which leads to the need of new development paradigms for Component Based Systems (CBS). Software development life cycle (SDLC) provides planned and systematic arrangement of activities to be carried out to deliver high quality products within time and budget. This paper presents a comparative study of component based software development life cycle models with their strengths and weaknesses.
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