基于网络文本与IPA模型分析的上海 辰山植物园旅游形象感知研究

生态空间承载着人类、动植物和自然生态多种过程,是生态系统服务功能的策源地和枢纽区。以实施生态空间管控为导引,借助多源数据和ArcGIS、C-Plan系统工具,核心应用系统保护规划(SCP)技术,建立生态服务测度下市域生态空间管控体系的构建方法。基于此,以哈尔滨为例,以不可替代性为表征实现生态空间测度指标综合与功效评价,构建生态空间关键区识别、功能区优划以及网络格局优化模型,提出"重点保护-分区指引-格局优化"的市域生态空间管控体系,为新型城镇化及绿色发展战略的实施奠定科学基础和提供技术支撑。     

Test suite reduction methods that decrease regression testing costs by identifying irreplaceable tests

ContextIn software development and maintenance, a software system may frequently be updated to meet rapidly changing user requirements. New test cases will be designed to ensure the correctness of new or modified functions, thus gradually increasing the test suite’s size. Test suite reduction techniques aim to decrease the cost of regression testing by removing the redundant test cases from the test suite and then obtaining a representative set of test cases that still yield a high level of code coverage.ObjectiveMost of the existing reduction algorithms focus on decreasing the test suite’s size. Yet, the differences in execution costs among test cases are usually significant and it may take a lot of execution time to run a test suite consisting of a few long-running test cases. This paper presents and empirically evaluates cost-aware algorithms that can produce the representative sets with lower execution costs.MethodWe first use a cost-aware test case metric, called Irreplaceability, and its enhanced version, called EIrreplaceability, to evaluate the possibility that each test case can be replaced by others during test suite reduction. Furthermore, we construct a cost-aware framework that incorporates the concept of test irreplaceability into some well-known test suite reduction algorithms.ResultsThe effectiveness of the cost-aware framework is evaluated via the subject programs and test suites collected from the Software-artifact Infrastructure Repository — frequently chosen benchmarks for experimentally evaluating test suite reduction methods. The empirical results reveal that the presented algorithms produce representative sets that normally incur a low cost to yield a high level of test coverage.ConclusionThe presented techniques indeed enhance the capability of the traditional reduction algorithms to reduce the execution cost of a test suite. Especially for the additional Greedy algorithm, the presented techniques decrease the costs of the representative sets by 8.10–46.57%.