Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design...Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.展开更多
In this article, we use the unrestricted two-regime autoregressive threshold model to test both nonlinearity and stationarity of China's real exchange rate against its Hong Kong and Macao special administrative re...In this article, we use the unrestricted two-regime autoregressive threshold model to test both nonlinearity and stationarity of China's real exchange rate against its Hong Kong and Macao special administrative regions(SARs). Our main finding is that China's real exchange rate is neither linear nor stationary, indicating that the purchasing power parity does not hold between China Mainland and its two SARs, which implies, to certain extent, the three economies may not meet the condition of constituting an optimal currency area.展开更多
单元测试用于检验软件单一模块的功能是否正确,是软件开发过程中的重要步骤,可以及时发现代码中的缺陷,提升软件的质量和可信度.由于手动编写单元测试费时费力,经常遗漏覆盖重要的代码逻辑.为此,研究者提出单元测试用例自动生成技术.近...单元测试用于检验软件单一模块的功能是否正确,是软件开发过程中的重要步骤,可以及时发现代码中的缺陷,提升软件的质量和可信度.由于手动编写单元测试费时费力,经常遗漏覆盖重要的代码逻辑.为此,研究者提出单元测试用例自动生成技术.近来,预训练大语言模型(large language models,LLM)已经广泛应用于代码生成相关任务.然而,当前在重要的系统级编程语言C上,还没有相关工作.为了填补这一空白,本文面向C程序设计并实现了基于LLM的单元测试用例生成方法LLM4CUTCG.该方法结合LLM多智能体交互和程序分析技术,客服了LLM内在问题.为了验证方法效果,收集了125个C语言目标程序,并针对这些程序生成测试用例.实验结果表明,LLM4CUTCG生成的测试行覆盖率为91.71%,测试预言正确率为50.05%.其覆盖率优于传统方法符号执行.展开更多
基于数据驱动的单元测试代码自动化生成技术存在覆盖率低和可读性差的问题,难以应对日益增长的测试需求。大语言模型(LLM)在代码生成任务中显示了极大的潜力,然而由于代码数据的功能风格和编码风格的差异,LLM面临灾难性遗忘和资源受限这...基于数据驱动的单元测试代码自动化生成技术存在覆盖率低和可读性差的问题,难以应对日益增长的测试需求。大语言模型(LLM)在代码生成任务中显示了极大的潜力,然而由于代码数据的功能风格和编码风格的差异,LLM面临灾难性遗忘和资源受限这2个挑战。为了解决这些问题,提出将编码风格和功能风格同步迁移微调的思想,并开发一种高效的LLM微调训练方法用于单元测试用例生成。首先,利用广泛使用的指令数据集对LLM进行指令对齐,并按任务类型对指令集分类;同时,提取并存储具有任务特征的权重增量;其次,设计一个自适应风格提取模块,该模块包含抗噪声干扰学习和编码风格回溯学习,以应对不同的代码编写风格;最后,在目标域分别对功能风格增量和编码风格增量进行联合训练,以实现在目标域低资源情况下的高效适配和微调。在SF110 Corpus of Classes数据集上的测试用例生成实验结果表明,所提方法的结果均优于对比方法,与主流代码生成LLM Codex、Code Llama和DeepSeek-Coder相比,所提方法的编译率分别提高0.8%、43.5%和33.8%、分支覆盖率分别提高3.1%、1.0%和17.2%;行覆盖率分别提高4.1%、6.5%和15.5%,验证了所提方法在代码生成任务上的优越性。展开更多
基金the National Natural Science Foundation of China(No.51175502)
文摘Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.
基金the National Natural Science Foundation of China(No.71373219)
文摘In this article, we use the unrestricted two-regime autoregressive threshold model to test both nonlinearity and stationarity of China's real exchange rate against its Hong Kong and Macao special administrative regions(SARs). Our main finding is that China's real exchange rate is neither linear nor stationary, indicating that the purchasing power parity does not hold between China Mainland and its two SARs, which implies, to certain extent, the three economies may not meet the condition of constituting an optimal currency area.
文摘单元测试用于检验软件单一模块的功能是否正确,是软件开发过程中的重要步骤,可以及时发现代码中的缺陷,提升软件的质量和可信度.由于手动编写单元测试费时费力,经常遗漏覆盖重要的代码逻辑.为此,研究者提出单元测试用例自动生成技术.近来,预训练大语言模型(large language models,LLM)已经广泛应用于代码生成相关任务.然而,当前在重要的系统级编程语言C上,还没有相关工作.为了填补这一空白,本文面向C程序设计并实现了基于LLM的单元测试用例生成方法LLM4CUTCG.该方法结合LLM多智能体交互和程序分析技术,客服了LLM内在问题.为了验证方法效果,收集了125个C语言目标程序,并针对这些程序生成测试用例.实验结果表明,LLM4CUTCG生成的测试行覆盖率为91.71%,测试预言正确率为50.05%.其覆盖率优于传统方法符号执行.
文摘基于数据驱动的单元测试代码自动化生成技术存在覆盖率低和可读性差的问题,难以应对日益增长的测试需求。大语言模型(LLM)在代码生成任务中显示了极大的潜力,然而由于代码数据的功能风格和编码风格的差异,LLM面临灾难性遗忘和资源受限这2个挑战。为了解决这些问题,提出将编码风格和功能风格同步迁移微调的思想,并开发一种高效的LLM微调训练方法用于单元测试用例生成。首先,利用广泛使用的指令数据集对LLM进行指令对齐,并按任务类型对指令集分类;同时,提取并存储具有任务特征的权重增量;其次,设计一个自适应风格提取模块,该模块包含抗噪声干扰学习和编码风格回溯学习,以应对不同的代码编写风格;最后,在目标域分别对功能风格增量和编码风格增量进行联合训练,以实现在目标域低资源情况下的高效适配和微调。在SF110 Corpus of Classes数据集上的测试用例生成实验结果表明,所提方法的结果均优于对比方法,与主流代码生成LLM Codex、Code Llama和DeepSeek-Coder相比,所提方法的编译率分别提高0.8%、43.5%和33.8%、分支覆盖率分别提高3.1%、1.0%和17.2%;行覆盖率分别提高4.1%、6.5%和15.5%,验证了所提方法在代码生成任务上的优越性。
