Regression Testing reduction is an essential phase in software Testing. In this step, the redundant and unnecessary cases are eliminated, whereas software accuracy and performance are not degraded. So far, various researches have been proposed in regression Testing reduction field. The main challenge in this area is to provide a method that maintain fault-detection capability while reducing Test suites. In this paper, a new Test suite reduction technique is proposed based on data mining. In this method, in addition to Test suite reduction, its fault-detection capability is preserved using both clustering and classification. In this approach, regression Test cases are reduced using a bi-criteria data mining-based method in two levels. In each level, the different and useful coverage criteria and clustering algorithms are used to establish a better compromise between Test suite size and the ability of reduced Test suite fault detection. The results of the proposed method have been compared to the effects of five other methods based on PSTR and PFDL. The experiments show the efficiency of the proposed method in the Test suite reduction in maintaining its capability in fault detection.

Now a day's software is the baseline for the success of any organization. There is a huge demand of quality software in the customer-oriented market. Regression Testing makes it possible but it’ s a costly affair. Regression Test suite minimization is way to reduce this cost but it is NP hard problem. This paper proposes an effective approach for regression Test suite minimization using Artificial Ecosystem Optimization algorithm. To improve its performance a modified Artificial Ecosystem Optimization algorithm is proposed for Test case minimization. To evaluate the performance of proposed approach experiment is conducted in controlled parameter setting on open-source subject program from SIR repository. The results are collected and analyzed in comparison to existing approaches using statistical Test. The Test results reflect the superiority of proposed approach.

Test-Driven Development (TDD) is one of the Test-first software production methods in which the production of each component of the code begins with writing the Test case. This method has been noticed due to many advantages, including the readable, regular and short code, as well as increasing the quality, productivity and reliability, and the possibility of regression Testing due to the creation of a comprehensive set of unit Tests. The large number of unit Test cases produced in this method is considered as a strong point in order to increase the reliability of the code, however, the repeated execution of Test cases increases the duration of the regression Testing in this method. The purpose of this article is to present an algorithm for selecting Test cases to reduce the time of the regression Test in TDD method. So far, various ideas have been proposed to select Test cases and reduce the regression Test time. Most of these ideas are based on programming language and software production methods. The idea presented in this article is based on the program difference method and the nature of the TDD method. In this method, meaningful semantic and structural connections are created between unit Tests and code blocks, and the Test case selection is done based on these relationships.

Nowadays using meta-heuristic algorithms besides T-way Testing strategy is increasing to generate minimum Test suites automatically. Genetic Algorithm, Ant Colony, Simulated Annealing, and Tabu Search play an important role in this regard. However, most of these algorithms cannot generate Test suits efficiently for T>3 due to their complex structure and complicated computations. In this paper, we propose a hybrid approach using hill climbing and bat search algorithms to minimum Test suit generation. Our proposed solution uses T-way strategy to Test suit generation for different configuration of the system. The proposed solution can generate Test suits up to T=10. Since one of the most important criterions for the evaluation of Test suits is the array size, hence we compare our results with other existing approaches in terms of this criterion. Our results show that our proposed solution outperforms other approaches.