The continuous integration infrastructure (CI servers) is commonly used as a shared test environment due to the need for collaborative and distributive development for the software products under growing scale and complexity in recent years. To ensure the stability of the CI servers, with the help of the constantly recorded measurement data of the servers, fault prediction is of great interest to software development companies. However, the lack of fault data is a typical challenge in learning the fault patterns directly. Alternatively, predicting the standard observations that represent the normal behavior of the CI servers can be viewed as an initial step toward fault prediction. Faults can then be identified and predicted by studying the difference between observed data and predicted standard data with enough fault data in the future. In this thesis, a long short-term memory (LSTM), a bidirectional LSTM (BiLSTM), and a vector autoregressive (VAR) models are developed. The models are compared on both one-step-ahead prediction and iteratively long-range prediction up to 60 steps (corresponds to 15 minutes for the CI servers analyzed in the thesis). To account for the uncertainties in the predictions, the LSTM-based models are trained to estimate predictive variance. The prediction intervals obtained are then compared with the VAR model. Moreover, since there are many servers in the CI infrastructure, it is of interest to investigate whether a model trained on one server can represent other servers. The investigation is carried out by applying the one-step-ahead LSTM model on a set of other servers and comparing the results. The LSTM model performs the best overall with only slightly better than the VAR model, whereas the BiLSTM model performs the worst in the one-step-ahead prediction. When taking the uncertainties into account, the LSTM model seems to estimate the assumed distribution the best with the highest log-likelihood. For long-range prediction, the VAR model surprisingly performs the best across almost all range lengths. Lastly, when applying the LSTM one-step-ahead model on the other servers, the performance differs from server to server, which indicates that it is less likely to achieve competitive performance when applying the same model on all servers.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-185929 |
| Date | January 2022 |
| Creators | Wang, Yiran |
| Publisher | Linköpings universitet, Statistik och maskininlärning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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