持续集成作为软件产品敏捷式开发的最佳实践，被广泛应用于实际软件产品开发中。持续集成测试要求快速测试和快速反馈，是确保代码质量的核心措施。在有限的资源下，实现面向持续集成的测试优化是一个非常具有挑战性的任务。本项目将持续集成测试优化定义为一个测试用例选择与优先排序相结合的混合优化模型，即选择一个可以满足约束的测试用例子集，并优先执行潜在发现错误的测试用例，以达到快速高效的目的。从理论上，以序贯决策理论为媒介，设计一个深度强化学习与持续集成测试优化的相融合的模型，构建基于学习的持续集成测试优化理论支撑体系。从应用技术上，在研究以测试用例优先排序和选择分别为目标的强化学习方法的基础上，重点研究两者的协作交流机制，形成基于深度强化学习的持续集成测试混合优化方法。在理论和应用技术研究的基础上，建立面向实际应用的持续集成测试优化实践，为智能化软件持续集成测试优化提供理论、技术和应用示范的支撑。

As one of the best software agile development practice, Continuous Integration (CI) has been widely adopted. The regression testing for continuous integration, which demands a shorter testing cycle and faster response for the failed test case, is the critical step to ensure the code quality integrated into the master branch. The challenge is how to optimize test cases for continuous integration within limited resources. In this proposal, the optimization for continuous integration testing is first defined as a mixed model with test case prioritization and test case selection, i.e., simultaneous selection and prioritization for test cases to achieve the goal of the shorter testing cycle and faster response. This proposal proposes a theoretical model that integrates deep reinforcement learning and continuous integration testing, via sequential decision-making theory, which forms the foundation of learning based continuous integration testing. In the application techniques, the reinforcement learning based test case prioritization and the reinforcement learning based test case selection are studied respectively first. Then, the cooperation and communication of these two approaches are explored for the mixed test case optimization for continuous integration testing. At last, a demonstration application satisfying the industrial requirements in continuous testing optimization will be built up based on the research of theories and applied technologies, where the achievements can provide strong theoretical, technical support and demonstration for intelligent integration testing.