Architectural refactorings can contain hundreds of steps and experienced developers could carry them out over several weeks. Moreover, developers need to explore a correct sequence of refactorings steps among many more incorrect alternatives. Thus, carrying out architectural refactorings is costly, risky, and challenging. In this paper, we present Refactoring Navigator: a tool-supported and interactive recommendation approach for aiding architectural refactoring. Our approach takes a given implementation as the starting point, a desired high-level design as the target, and iteratively recommends a series of refactoring steps. Moreover, our approach allows the user to accept, reject, or ignore a recommended refactoring step, and uses the user's feedback in further refactoring recommendations. We evaluated the effectiveness of our approach and tool using a controlled experiment and an industrial case study. The controlled experiment shows that the participants who used Refactoring Navigator accomplished their tasks in 77.4% less time and manually edited 98.3% fewer lines than the control group. The industrial case study suggests that Refactoring Navigator has the potential to help with architectural refactorings in practice.

Developers often need to modify pasted code when programming with copy-and-paste practice. Some modifications on pasted code could involve lots of editing efforts, and any missing or wrong edit could incur bugs. In this paper, we propose a clone-based and interactive approach to recommending where and how to modify the pasted code. In our approach, we regard clones of the pasted code as the results of historical copy-and-paste operations and their differences as historical modifications on the same piece of code. Our approach first retrieves clones of the pasted code from a clone repository and detects syntactically complete differences among them. Then our approach transfers each clone difference into a modification slot on the pasted code, suggests options for each slot, and further mines modifying regulations from the clone differences. Based on the mined modifying regulations, our approach dynamically updates the suggested options and their ranking in each slot according to developer's modifications on the pasted code. We implement a proof-of-concept tool CCDemon based on our approach and evaluate its effectiveness based on code clones detected from five open source projects. The results show that our approach can identify 96.9% of the to-be-modified positions in pasted code and suggest 75.0% of the required modifications. Our human study further confirms that CCDemon can help developers to accomplish their modifications of pasted code more efficiently.