使用进化算法的多目标测试用例最小化:回顾

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2017-04-01 DOI:10.1109/ICECA.2017.8203698

Vandana, Ajmer Singh

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引用次数: 4

摘要

软件测试是各种软件开发生命周期模型中的原始阶段之一，它消耗了大约70%的开发时间和40%的总体预算成本。目前，在软件测试阶段使用自动化测试工具以及不同的元启发式算法来减少测试各种程序代码的工作量和时间，这些工具的工作原理与简单的测试技术相似，但在程序复杂性较高的情况下，它们明显优于测试技术。最近的研究表明，各种进化算法(EA)如人工免疫系统(AIS)、粒子群优化(PSO)、模拟退火、人工蜂群(ABC)、杜鹃搜索算法(CSA)、蚁群优化(ACO)等正在软件工程领域得到功能化，以获得最优解。这篇回顾文章演示了使用这些进化算法最小化测试用例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multi-objective test case minimization using evolutionary algorithms: A review

Software testing is one of the primal phase in various software development lifecycle models and consumes approximately 70% of development time and 40% cost of the overall budget. Nowadays automated testing tools along with different meta-heuristic algorithms which work similarly as simple testing techniques but they significantly outperforms when the complexity of the program is high are used in software testing phase to reduce the effort and time to test various program codes. Recent studies shows that various Evolutionary Algorithms (EA) like Artificial Immune System (AIS), Particle Swarm Optimization (PSO), Simulated annealing, Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), Ant colony optimization (ACO) are being functionalized in the field of Software Engineering to obtain optimal solutions. This review paper demonstrates the minimization of test cases using these evolutionary algorithms.

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2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

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