Context: Most research in software clustering and remodularisation typically concludes by recommending the refactoring operations without further insight into the practicality of the proposed technique. Developers might be hesitant to follow through with the refactoring suggestions due to the uncertainty in the effort needed. Objective: This work aims to address this gap by introducing an effoRt Estimation AppRoach foR softwAre clusteriNG-based rEmodularisation (REARRANGE) to close the loop in extant software clustering and remodularisation research by estimating the time required to carry out the suggested refactoring operations based on the history of the evolution of the software. By providing tangible estimates of refactoring effort in person-hours, we can inform developers of complex and time-consuming refactoring operations that will help prioritise refactoring efforts, allowing practitioners to weave in these activities during sprint planning. Method: REARRANGE builds a machine learning model to predict effort estimation based on past commit activity which extracts Software Features (lines of code, number of methods), Refactoring Features (refactoring type, source and destination) and Dependency Features (dependencies between classes). REARRANGE is then compared against sanity checks, baseline effort estimation models, and state-of-the-art software estimation models. We also attempt to cross-validate REARRANGE's effort estimation with software developers. Results: Experimented through 25 open-source Java-based projects, the proposed approach estimated the refactoring effort of the test subjects with a Mean Absolute Error (MAE) of 5.47 person-hours against the MAE of the next-best approach of 453.31 person-hours. Based on a survey conducted among software developers, REARRANGE consistently delivers accurate estimates in 93.6% of cases. Conclusion: The lack of a direct comparison for REARRANGE highlights the need for a refactoring effort-focused estimation model that provides tangible effort estimates in person-hours for refactoring operations. Only then can developers selectively choose relevant refactoring operations while considering the available time and budget constraints, bridging the gap between software clustering research and real-world application.

Maintenance of existing software requires a large amount of time for comprehending the source code. The architecture of a software, however, may not be clear to maintainers if up-to-date documentations are not available. Software clustering is often used as a remodularisation and architecture recovery technique to help recover a semantic representation of the software design. However, due to the diverse domain and structure of software systems, the suitability of different clustering techniques for different software systems are not investigated thoroughly. Research that introduce new clustering techniques usually validate their approaches on a specific domain, which might limit its generalisability. If the chosen test subjects only represent a narrow perspective of the whole picture, researchers risk not being able to address the external validity of their findings. This work aims to fill this gap by introducing a new approach, Explaining Software Clustering for Remodularisation (E-SC4R), to evaluate the effectiveness of different software clustering approaches. This work focuses on hierarchical clustering and Bunch clustering algorithms and provides information about their suitability according to the features of the software, which, as a consequence, enables the selection of the optimum technique for a particular software system. The E-SC4R framework is able to characterise both the strengths and weaknesses of the analysed software clustering algorithms using software features extracted from the code. The proposed approach also provides a better understanding of the algorithms’ behaviour by showing a 2D representation of the effectiveness of clustering techniques on the feature space through the application of dimensionality reduction techniques.