基于神经网络的回归测试用例优化研究

Optimized research of regression testing cases based on neural network

回归测试是指修改了源代码后,重新进行测试以确认已发现的缺陷是否修复和检测修改是否引入了新的错误或导致其他代码产生错误,在测试过程中占有很大的工作量比重。通过分析神经网络的基本原理,并将BP算法的思想引入到回归测试的用例集选取中,介绍了回归测试用例包选取的算法,通过样本训练,筛选出代码改动后可能影响到的功能,从而可以筛选出优先级别较高的用例。最后,通过测试实践的积累,总结了一套高效易行的回归测试策略。

Regression testing means after modifying the source code,re？testing to confirm whether the discovered defect is repaired,and whether detection and modification have brought in a new bug or caused the errors in other codes which possesses a large proportion of the workload during testing procedure. The fundamental principle of neural network is analyzed,and the thought of BP algorithm is introduced into the case set selection of regression testing. The algorithm to select regression testing case package is presented. The functions which may be influenced by code modification are screened out by samples training, and the higher priority use case can be screened out. A set of regression testing strategy with high efficient and easy operation was summed up through the accumulation of testing practice.