CARP: Context-Aware Reliability Prediction of Black-Box Web Services
Jieming Zhu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu

TL;DR
CARP is a novel context-aware approach for predicting the reliability of black-box web services using historical data, significantly improving accuracy by addressing data sparsity and context variability.
Contribution
It introduces a new reliability prediction model that leverages user context and historical data to enhance accuracy for black-box web services.
Findings
Improves reliability prediction accuracy by about 41% in MAE.
Reduces RMSE by approximately 38%.
Effective with as little as 5% of data available.
Abstract
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…
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Taxonomy
TopicsSoftware Reliability and Analysis Research · Software System Performance and Reliability · Data Quality and Management
