混合优化与约束处理用于组合测试案例优先级问题
Selvakumar J1, Sudhir Sharma2, Mukesh Kumar Tripathi3
1Department of Computer Science & Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
本研究介绍了软件开发中测试案例优先级 (TCP) 的新优化算法. 分数混合型基于Leader的优化 (FHLO) 有效地对测试案例进行优先排序,以更早地检测故障并降低成本.
科学领域:
- 软件工程 软件工程 软件工程
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 软件测试对于确保软件质量和有效性至关重要.
- 测试案例优先级 (TCP) 旨在尽量减少测试套件的执行时间.
- 现有的TCP研究往往侧重于时间和故障限制.
研究的目的:
- 为结合式TCP带有约束处理引入一种新的优化算法.
- 改善早期故障检测并降低回归测试成本.
- 根据故障检测和分支覆盖范围来优先考虑测试案例.
主要方法:
- 开发基于分数混合的领导优化 (FHLO) 算法.
- FHLO应用于组合测试案例优先级问题.
- 优先级取决于最大化分支机构覆盖率的平均百分比 (APBC) 和检测故障的平均百分比 (APFD).
主要成果:
- FHLO算法实现了0.966.6的最大APFD.
- 该FHLO算法实现了0.888.88的最大APBC.
- 在对检测故障的测试案例进行优先排序方面表现出有效性.
结论:
- FHLO算法是测试案例优先级的有效技术.
- FHLO增强了早期故障检测,并优化了测试执行.
- 该算法在APFD和APBC指标中提供了显著的改进.
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