Parallel programs need to manage the time trade-off between synchronization and computation. A high parallelism may decrease computing time but meanwhile increase synchronization cost. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. A way to reduce conflicts is by adjusting parallelisms. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is costly and error-prone. Hence, it becomes necessary to adopt a dynamical tuning-configuration strategy to better manage a STM system. Autonomic computing offers designers a framework of methods and techniques to build systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low.