Logs and models in engineering complex embedded systems

Complex embedded systems, such as robotics, automotive and high-tech manufacturing, are hard to maintain due to their complex nature. To advance our understanding of the software engineering practice for complex embedded systems, we conducted a series of empirical studies at ASML, a leading manufacturer of lithography machines for semi-conductor industry. We started with an interview study exploring how developers use execution logs, essential artifacts that capture the runtime behavior of software systems. The empirical insights obtained from this study led us to explore subtopics about model inference from logs, modeling practice and log comparison. Motivated by the observation that developers often manually sketch behavioral models based on logs, we propose a model inference technique that can extract models by combining log analysis, and analysis of a running system under stimuli. As observed in this model inference study, the transition from code to models requires developers to work with a hybrid system which consists of handwritten code and models. We then study modeling practices and the roles of model in such hybrid systems. Particularly, we study why developers violate modeling guidelines, providing implications for researchers and tool builders to support developers in modeling complex embedded systems. Another interesting observation from the interview study is that developers face challenges in comparing multiple logs generated from such systems. We therefore conduct a literature study to provide an overview of the existing techniques and identify the limitations of the existing techniques. In this project, we study logs and models in complex embedded systems, providing tool builders, researchers and practitioners with implications to facilitate log analysis, model inference, modeling practice and log comparison.