چکیده:
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.
خلاصه ماشینی:
Regression Test Suite Minimization Using Modified Artificial Ecosystem Optimization Algorithm Abhishek Singh Verma* *Corresponding Author, Assistant Professor, CSED, School of Engineering & Technology, Sharda University, Greater Noida, India.
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.
There exist three different types of regression test suite optimization such as: Minimization, Selection and Prioritization (Yoo & Harman, 2010).
In this paper a new approach for Test Suite Minimization using Hybrid Artificial Ecosystem Optimization algorithm has been proposed.
The main contribution of the paper is as follows: Proposed Test Case Minimization approach using Artificial Ecosystem Optimization (AEO) algorithm.
To find out an optimal subset of test suite an another heuristic approach based on GRE (Greedy Redundant and Essential) has been proposed (Chen & Lau, 1998a).
proposed multi objective approach for test case selection problem by utilizing multi objective genetic algorithm and claimed better performance than random search approach (Loiola & Maia, 2009).
A fuzzy logic based approach has been proposed for multi objective test suite minimization problem (Haider et al.
The approaches discussed above claim that both heuristic and metaheuristic-based approaches have been proposed to address the test suite minimization problem but still a scope of optimization exists.
To these issues this paper proposes an effective Modified Artificial Ecosystem optimization-based approach for test suite minimization.