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Orientation-based Ant colony algorithm for synthesizing the test scenarios in UML activity diagram
Information and Software Technology ( IF 3.9 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.infsof.2020.106292
Vinay Arora , Maninder Singh , Rajesh Bhatia

Context

The model-based analysis is preferred over the code-based analysis as it speeds up the development process and directs the guiding effort. In the software industry, the Unified Modeling Language (UML) is a set standard followed by the developers as well as system analysts to extract all attainable paths of controls, usually known as scenarios under an activity diagram.

Objective

In this manuscript, a bio-inspired methodology has been applied on concurrent sub-part of a UML activity diagram to fetch various feasible test scenarios.

Method

The food search pattern of an ant has been taken as a base heuristic. An orientation factor has been introduced in the existing ant colony optimization algorithm. Experiments have been performed using three student projects, five synthetic models and an openly available model repository named LINDHOLMEN data-set at Github.

Results

The statistical analysis has validated the results obtained through various existing approaches and the proposed approach. Experimentation shows that the orientation-based ant colony algorithm has produced better results as compared to the existing Genetic Algorithm (GA) and Ant Colony Optimization (ACO) on the basis of feasible test scenarios generated.



中文翻译:

基于方向的蚁群算法,用于在UML活动图中综合测试场景

语境

基于模型的分析比基于代码的分析更可取,因为它可以加快开发过程并指导指导工作。在软件行业,统一建模语言(UML)是一种设置的标准,开发人员和系统分析人员都遵循该标准来提取控件的所有可获得路径,通常称为活动图下的场景。

目的

在此手稿中,已将生物启发性方法应用于UML活动图的并行子部分,以获取各种可行的测试方案。

方法

蚂蚁的食物搜索模式已被视为基本启发式算法。在现有的蚁群优化算法中已经引入了定向因子。实验是使用三个学生项目,五个合成模型以及Github上名为LINDHOLMEN数据集的公开可用模型存储库进行的。

结果

统计分析已经验证了通过各种现有方法和建议方法获得的结果。实验表明,基于现有的遗传算法(GA)和蚁群优化(ACO),基于方向的蚁群算法在可行的测试场景的基础上产生了更好的结果。

更新日期:2020-03-01
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