Due to the conflicting nature of quality measures, there are always multiple refactoring options to fix quality issues. Thus, interaction with developers is critical to inject their preferences. While several interactive techniques have been proposed, developers still need to examine large numbers of possible refactorings, which makes the interaction time-consuming. Furthermore, existing interactive tools are limited to the ”objective space” to show developers the impacts of refactorings on quality attributes. However, the “decision space” is also important since developers may want to focus on specific code locations. In this paper, we propose an interactive approach that enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Developers may be interested in looking at refactoring strategies that can improve a specific quality attribute, such as extendibility (objective space), but such strategies may be related to different code locations (decision space). A plethora of solutions is generated at first using multi-objective search that tries to find the possible trade-offs between quality objectives. Then, an unsupervised learning algorithm clusters the trade-off solutions based on their quality metrics, and another clustering algorithm is applied within each cluster of the objective space to identify solutions related to different code locations. The objective and decision spaces can now be explored more efficiently by the developer, who can give feedback on a smaller number of solutions. This feedback is then used to generate constraints for the optimization process, to focus on the developer's regions of interest in both the decision and objective spaces. A manual validation of selected refactoring solutions by developers confirms that our approach outperforms state of the art refactoring techniques.

The documentation of code changes is significantly important but developers ignore it, most of the time, due to the pressure of the deadlines. While developers may document the most important features modification or bugs fixing, recent empirical studies show that the documentation of quality improvements and/or refactoring is often omitted or not accurately described. However, the automated or semi-automated documentation of refactorings has not been yet explored despite the extensive work on the remaining steps of refactoring including the detection, prioritization and recommendation. In this paper, we propose a semi-automated refactoring documentation bot that helps developers to interactively check and validate the documentation of the refactorings and/or quality improvements at the file level for each opened pull-request before being reviewed or merged to the master. The bot starts by checking the pullrequest if there are significant quality changes and refactorings at the file level and whether they are documented by the developer. Then, it checks the validity of the developers description of the refactorings, if any. Based on that analysis, the documentation bot will recommend a message to document the refactorings, their locations and the quality improvement for that pull-request when missing information is found. Then, the developer can modify his pull request description by interacting with the bot to accept/modify/reject part of the proposed documentation. Since refactoring do not happen in isolation most of the time, the bot is documenting the impact of a sequence of refactorings, in a pull-request, on quality and not each refactoring in isolation. We conducted a human survey with 14 active developers to manually evaluate the relevance and the correctness of our tool on different pull requests of 5 open source projects and one industrial system. The results show that the participants found that our bot facilitates the documentation of their quality-related changes and refactorings.