Performance Analysis of CI/CD Pipelines Using (Colored) Generalized Stochastic Petri Nets

DevOps is enjoying widespread adoption by enterprises. Many of them are utilizing CI/CD pipelines. These allow them to define a chain of processing, in which elements of the DevOps toolchain, such as building, testing, and deploying, are executed. As software can only reach users through this pipeline, its speed is of quintes- sential concern, as delays or failures of the pipeline can cause an abrupt halt to development and deployment. However, there is a severe lack in methods to evaluate the speed of a pipeline. Thus, we propose an approach to detect performance issues of pipelines using Petri Nets. Additionally, we propose an approach to predict to what degree pipeline execution must be adjusted, in order to prevent performance issues entirely. Our approach utilizes StalkCD, which allows the transformation of pipelines into BPMN and back. We use the model it creates, and transform it into a Petri Net instead. We then run a steady state simulation on the Petri Net in order to get estimates on how the pipeline will perform under various loads. As StalkCD currently only transforms Jenkins pipelines, the pat- terns we use to create our Petri Net model were derived primarily from Jenkins pipelines as well. Additionally, to obtain test-related information, such as service rates of pipeline elements, we have de- veloped scripts to transform data provided by various Jenkins APIs into data models which are used by our transformation. To evaluate the validity of our approach, we analyze the speed of simulation of our Petri Net and how accurate our predictions, stemming from the Petri Net simulation, are to data obtained from actual pipeline executions. Our result is that the Petri net model we created can not make accurate predictions of Jenkins pipelines. We discovered this is due to patterns in Jenkins which we did not or could not cover in our model. Additionally, we discovered our evaluation could not produce results necessary to validate our approach. To create an accurate model and evaluate its predictions would require a massive amount of research and effort