HDCCSR: Software Self-Awareness Using Dynamic Analysis and Markov Models

长寿的、自主的软件系统需要持续的自我评估，这意味着对预期和实际刺激之间的比较，作为自我诊断和修复的先决条件。该项目介绍了动态分析和机器学习技术在行为分类中的应用。拟议的工作将-通过探索如何最有效地应用机器学习技术来改进自我评估过程，来解决基于软件的可靠计算系统的基本研究和教育问题；-以原型工具或方法的形式开发研究产品，将马尔可夫模型和集群分析应用于程序行为数据的评估；-提供适合于衡量研究产品的影响的可靠性属性，例如一组马尔可夫模型成功描述软件系统的行为的程度；-提供对研究产品的经验评估/验证以及相关的测试数据，以验证方法的有效性拟议的研究将提供关于使用马尔可夫模型编码行为模型的经验数据，以及用于执行进一步实验的方法和基础设施。由此产生的为软件提供行为模型以帮助自我意识的技术将促进实时系统开发的研究。

Long-lived, autonomous software systems require ongoing self-assessment, which implies a comparison between expected and actualstimuli, as a prerequisite to self-diagnosis and repair. This project addresses the use of dynamic analysis and machinelearning techniques for behavior classification. The proposed work will- address fundamental research and education issues in dependable software-based computing systems by exploring how machine learning techniques can most effectively be applied to improve the self-assessment process;- develop research products in the form of prototype tools or methodologies, applying Markov models and cluster analysis to the assessment of program behavior data;- provide dependability attributes that are suitable for measuring the impact of the research products, such as the extent to which a set of Markov models successfully describes the behavior of a software system;- provide empirical evaluation/validation of the research products together with associated test data to validate the effectiveness of the approachThe proposed research will provide empirical data on the use of Markov models to encode behavioral models, and methodologiesand infrastructure for use in performing further experimentation.The resulting techniques for provisioning software with behavior models to aid in self-awareness will promote research on development of real-time systems.