A survey on Test Suite Reduction frameworks and tools <s> Hybrid Solution and Agile Software Development <s> Software Development is moving towards a more agile and more flexible approach. It turns out that the traditional "waterfall" model is not supportive in an environment where technical, financial and strategic constraints are changing almost every day. But what is agility? What are todays major approaches? And especially: What is the impact of agile development principles on the development teams, on project management and on software architects? How can large enterprises become more agile and improve their business processes, which have been existing since many, many years? What are the limitations of Agility? And what is the right balance between reliable structures and flexibility? This book will give answers to these questions. A strong emphasis will be on real life project examples, which describe how development teams have moved from a waterfall model towards an Agile Software Development approach. <s> BIB001 </s> A survey on Test Suite Reduction frameworks and tools <s> Hybrid Solution and Agile Software Development <s> Test suite minimisation techniques seek to reduce the effort required for regression testing by selecting a subset of test suites. In previous work, the problem has been considered as a single-objective optimisation problem. However, real world regression testing can be a complex process in which multiple testing criteria and constraints are involved. This paper presents the concept of Pareto efficiency for the test suite minimisation problem. The Pareto-efficient approach is inherently capable of dealing with multiple objectives, providing the decision maker with a group of solutions that are not dominated by each other. The paper illustrates the benefits of Pareto efficient multi-objective test suite minimisation with empirical studies of two and three objective formulations, in which multiple objectives such as coverage and past fault-detection history are considered. The paper utilises a hybrid, multi-objective genetic algorithm that combines the efficient approximation of the greedy approach with the capability of population based genetic algorithm to produce higher-quality Pareto fronts. <s> BIB002 </s> A survey on Test Suite Reduction frameworks and tools <s> Hybrid Solution and Agile Software Development <s> Search-based optimization techniques have been applied to structural software test data generation since 1992, with a recent upsurge in interest and activity within this area. However, despite the large number of recent studies on the applicability of different search-based optimization approaches, there has been very little theoretical analysis of the types of testing problem for which these techniques are well suited. There are also few empirical studies that present results for larger programs. This paper presents a theoretical exploration of the most widely studied approach, the global search technique embodied by Genetic Algorithms. It also presents results from a large empirical study that compares the behavior of both global and local search-based optimization on real-world programs. The results of this study reveal that cases exist of test data generation problem that suit each algorithm, thereby suggesting that a hybrid global-local search (a Memetic Algorithm) may be appropriate. The paper presents a Memetic Algorithm along with further empirical results studying its performance. <s> BIB003
Hybrid solutions for optimization problems show significant improvements in existing results by combining the strong points of two different TSR approaches BIB002 . Future tools can use the strengths of existing TSR approaches such as coverage-based and search-based to find a more effective solution for TSR problem BIB003 . For example, the future tool first selects a set of diverse test cases by employing a search-based approach. Next, the adequacy of computed solution in terms of attained coverage is computed by using the coveragebased approach. Consequently, the integration of search-based and coverage-based approaches helps to provide an optimal solution of TSR problem with respect to high coverage of SUT. Notice that achieved coverage of the computed solution acts as a quality indicator, which is beneficial in deciding whether the test optimization process should be continued or stopped. The TSR tool is highly beneficial to support agile software-development process BIB001 . One of the important practices of agile software development is on the notion "testing early and testing often". In this scenario, test cases signify instant feedback to the tester, whether the tested portion of a program is free of error or introduces new regression errors. Such tool would be also practical for web application testing using online mode of computation.