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.

Refactoring studies either aggregated quality metrics to evaluate possible code changes or treated them separately to find trade-offs. For the first category of work, it is challenging to define upfront the weights for the quality objectives since developers are not able to express them upfront. For the second category of work, the number of possible trade-offs between quality objectives is large which makes developers reluctant to look at many refactoring solutions. In this paper, we propose, for the first time, a way to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on his preferences. The first step consists of using a multi-objective search to generate different possible refactoring strategies by finding a trade-off between several conflicting quality attributes. Then, an unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and to reduce the number of refactoring options to explore. Finally, the extracted preferences from the developer are used to transform the multi-objective search into a mono-objective one by taking the preferred cluster of the Pareto front as the initial population for the mono-objective search and generating an evaluation function based on the weights that are automatically computed from the position of the cluster in the Pareto front. Thus, the developer will just interact with only one refactoring solution generated by the mono-objective search. We selected 32 participants to manually evaluate the effectiveness of our tool on 7 open source projects and one industrial project. The results show that the recommended refactorings are more accurate than the current state of the art.