  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.
