CARP：基于上下文感知的黑盒Web服务可靠性预测
CARP: Context-Aware Reliability Prediction of Black-Box Web Services
摘要：
可靠性预测是软件可靠性工程中的一项重要任务，在过去的几十年中得到了广泛的研究。然而，建模和预测用户感知的可靠性的黑箱服务仍然是一个开放的研究问题。软件服务，如Web服务和Web API，通常通过互联网向用户提供黑盒功能，从而导致缺乏其内部信息进行可靠性分析。此外，用户感知的服务可靠性不仅取决于服务本身，而且在很大程度上取决于调用上下文（例如，服务工作负载、网络条件），从而传统的可靠性模型变得无效和不合适。为了解决这些新的挑战所带来的黑盒服务，在本文中，我们提出了CARP，一个新的contextaware可靠性预测方法，利用历史使用数据从用户构建上下文感知的可靠性模型，并进一步提供在线可靠性预测结果给用户。通过上下文感知的可靠性建模，CARP能够缓解数据稀疏性问题，严重限制了其他现有方法的预测精度。初步评估结果表明，CARP能显著提高可靠性预测精度，当只有5%的数据可用时，MAE约为41%，RMSE约为38%。
英文摘要：
Reliability prediction is an important task in software reliability engineering, which has been widely studied in the last decades. However, modelling and predicting user-perceived reliability of black-box services remain an open research problem. Software services, such as Web services and Web APIs, generally provide black-box functionalities to users through the Internet, thus leading to a lack of their internal information for reliability analysis. Furthermore, the user-perceived service reliability depends not only on the service itself, but also heavily on the invocation context (e.g., service workloads, network conditions), whereby traditional reliability models become ineffective and inappropriate. To address these new challenges posed by blackbox services, in this paper, we propose CARP, a new contextaware reliability prediction approach, which leverages historical usage data from users to construct context-aware reliability models and further provides online reliability prediction results to users. Through context-aware reliability modelling, CARP is able to alleviate the data sparsity problem that heavily limits the prediction accuracy of other existing approaches. The preliminary evaluation results show that CARP can make a significant improvement in reliability prediction accuracy, e.g., about 41% in MAE and 38% in RMSE when only 5% of the data are available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
