使用人工智能支持持续集成的测试选择

Maria Laura Brzezinski Meyer
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引用次数: 0

摘要

敏捷方法在工业界得到了越来越多的应用,它将流程分解为计划、执行和评估的周期。在软件开发领域,一种被称为持续集成的敏捷方法被广泛用于自动地将来自不同开发人员的代码更改集成到相同的软件中。然后,可以对每个新构建进行测试,以确保修改不会干扰已经验证的代码的其余部分。尽管回归测试非常重要,但它通常是项目中成本最高的部分。由于执行时间的原因,重新测试每个新软件版本的所有测试是很费力的,而且通常在所有测试完成之前,一个新的软件版本已经准备好要测试了。为了改进回归测试结果,可以进行选择。通过在正确的时间选择正确的测试,可以避免使用所有的测试目录来发现被测试软件中的错误。这项工作的目的是开发一种方法,使用人工智能算法为每个版本选择要执行的测试。学习算法可以找到测试用例之间的模式和相似之处,以帮助了解哪一个更有可能暴露错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TSAI - Test Selection using Artificial Intelligence for the Support of Continuous Integration
The agile methodology has been increasingly deployed in the industry world, breaking the process into cycles of planning, executing, and evaluating. In the software development domain, an agile method named continuous integration is widely used to automatically integrate code changes from different developers into the same software. Then, each new build can be tested to make sure that the modifications did not interfere with the rest of the already verified code. Despite being very important, regression tests are usually the costliest part of a project. It is laborious to retest all tests of each new software version due to the time it takes to perform and often, before all tests are finished, a new software version is ready to be tested. To improve regression tests results, a selection can be done. By selecting the right tests at the right moment, the use of all test catalogs can be avoided to find faults in the software tested. The aim of this work is to develop a method to select tests to be executed for each version using artificial intelligence algorithms. Learning algorithms can find patterns and similarities between test cases to help knowing which one has a higher probability to expose a fault.
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