Inferring dependencies among web services with predictive and statistical analysis of system logs

Software system behaviour analysis is a challenging research problem in software engineering. The main reason for this is the lack of real data from large industrial systems. Softtech Inc. is a subsidiary of a large private bank in Turkey and this study is aimed to analyse mapping the services architecture and the software system health of a particular department at Softtech using specific software web service logs. The services that are the subject of this study consist of 196 web services related to credit and credit card application transactions from various channels. While these processes are related to similar applications, they call various web services that perform different operations in the background. Related services account for 2 million logs daily. We have conducted empirical and statistical analysis on the data, in order to infer the correlations and dependencies among the observed web services. Hypothetically, we think there are 3 types of dependencies between the web services. In our experiments, we used average response times and the number of times web services are called at specific time intervals as input data. The results suggest that they can be used for inferring that there is a dependency between two web services. In this preliminary work for dependency inference from unstructured web services' log data, we have utilized simple statistical analysis tools to derive important insight about the collection of services under our observation. The results have encouraged us to carry on with a more detailed analysis approach to further advance our research efforts.