Excessively large classes that encapsulate multiple responsibilities are challenging to comprehend and maintain. Addressing this issue, several Extract Class refactoring tools have been proposed, employing a two-phase process: identifying suitable fields or methods for extraction, and implementing the mechanics of refactoring. These tools traditionally generate an intra-class dependency graph to analyze the class structure, applying hard-coded rules based on this graph to unearth refactoring opportunities. Yet, the graph-based approach predominantly illuminates direct, “one-to-one” relationship between pairwise entities. Such a perspective is restrictive as it overlooks the complex, “one-to-many” dependencies among multiple entities that are prevalent in real-world classes. This narrow focus can lead to refactoring suggestions that may diverge from developers’ actual needs, given their multifaceted nature. To bridge this gap, our paper leverages the concept of intra-class dependency hypergraph to model one-to-many dependency relationship and proposes a hypergraph learning-based approach to suggest Extract Class refactoring opportunities named HECS. For each target class, we first construct its intra-class dependency hypergraph and assign attributes to nodes with a pre-trained code model. All the attributed hypergraphs are fed into an enhanced hypergraph neural network for training. Utilizing this trained neural network alongside a large language model (LLM), we construct a refactoring suggestion system. We trained HECS on a large-scale dataset and evaluated it on two real-world datasets. The results show that demonstrates an increase of 38.5% in precision, 9.7% in recall, and 44.4% in f1-measure compared to 3 state-of-the-art refactoring tools including JDeodorant, SSECS, and LLMRefactor, which is more useful for 64% of participants. The results also unveil practical suggestions and new insights that benefit existing extract-related refactoring techniques.

HECS is an advanced tool designed for Extract Class refactoring by leveraging hypergraph learning to model complex dependencies within large classes. Unlike traditional tools that rely on direct one-to-one dependency graphs, HECS uses intra-class dependency hypergraphs to capture one-to-many relationships. This allows HECS to provide more accurate and relevant refactoring suggestions. The tool constructs hypergraphs for each target class, attributes nodes using a pre-trained code model, and trains an enhanced hypergraph neural network. Coupled with a large language model, HECS delivers practical refactoring suggestions. In evaluations on large-scale and real-world datasets, HECS achieved a 38.5% increase in precision, 9.7% in recall, and 44.4% in f1-measure compared to JDeodorant, SSECS, and LLMRefactor. These improvements make HECS a valuable tool for developers, offering practical insights and enhancing existing refactoring techniques.

Methods implemented in incorrect classes will cause excessive reliance on other classes than their own, known as a typical code smell symptom: feature envy, which makes it difficult to maintain increased coupling between classes. Addressing this issue, several Move Method refactoring tools have been proposed, employing a two-phase process: identifying misplaced methods to move and appropriate classes to receive, and implementing the mechanics of refactoring. These tools traditionally use hard-coded metrics to measure correlations between movable methods and target classes and apply heuristic thresholds or trained classifiers to unearth refactoring opportunities. Yet, these approaches predominantly illuminate pairwise correlations between methods and classes while overlooking the complex and complicated dependencies binding multiple code entities within these methods/classes that are prevalent in real-world cases. This narrow focus can lead to refactoring suggestions that may diverge from developers' actual needs. To bridge this gap, our paper leverages the concept of inter-class code entity dependency hypergraph to model complicated dependency relationships involving multiple code entities within various methods/classes and proposes a hypergraph learning-based approach to suggest Move Method refactoring opportunities named HMove. We first construct inter-class code entity dependency hypergraphs from training samples and assign attributes to entities with a pre-trained code model. All the attributed hypergraphs are fed into a hybrid hypergraph neural network for training. Utilizing this trained neural network alongside a large language model, we construct a refactoring suggestion system. We trained HMove on a large-scale dataset and evaluated it on two real-world datasets. The results show that demonstrates an increase of 27.8% in precision, 2.5% in recall, and 18.5% in f1-measure compared to 9 state-of-the-art refactoring tools, which is more useful for 68% of participants. The results also unveil practical suggestions and new insights that benefit existing feature envy-related refactoring techniques.

Extract Method is one of the most frequently performed refactoring operations for the decomposition of large and complex methods, which can also be combined with other refactoring operations to remove a variety of design flaws. Several Extract Method refactoring tools have been proposed based on the quantification of extraction criteria. To the best of our knowledge, state-of-the-art related techniques can be broadly divided into two categories: the first line is non-machine-learning-based approaches built on heuristics, and the second line is machine learning-based approaches built on historical data. Most of these approaches characterize the extraction criteria by deriving software metrics from fine-grained code properties. However, in most cases, these metrics can be challenging to concretize, and their selections and thresholds also largely rely on expert knowledge. Thus, in this paper, we propose an approach to automatically recommend Extract Method refactoring opportunities named REMS via mining multi-view representations from code property graph. We fuse various representations together using compact bilinear pooling and further train machine learning classifiers to guide the extraction of suitable lines of code as new method. We evaluate our approach on two publicly available datasets. The results show that our approach outperforms five state-of-the-art refactoring tools including GEMS, JExtract, SEMI, JDeodorant, and Segmentation in effectiveness and usefulness. Our approach demonstrates an increase of 29% in precision, 15% in recall, and 23% in f1-measure. The results also unveil practical suggestions and provide new insights that benefit additional extract-related refactoring techniques.

Efficient path planning is the key enabling technology for the realization of intelligent transportation systems (ITS). However, due to poor real-time performance and lack of effective incentive methods, it is difficult for traditional path planning schemes to significantly improve the efficiency of traffic management. In addition, existing solutions that use driving distance and driving time as indicators cannot meet the personalized requirements of vehicle users. To this end, by considering the personalized requirements of vehicle users, we propose a digital-twin (DT) enabled path planning scheme to facilitate traffic management. To be specific, based on the collection of traffic data, we first establish a DT architecture for traffic scheduling to reduce the delay of path planning. Then, according to the traffic density of different road sections, we regard road sections as resources and set different rewards for different road sections to encourage vehicles to obey the scheduling instructions. In addition, by jointly considering the driving time and rewards, we further design personalized utility models to map the requirements of different vehicle users. After that, based on the personalized requirement of the vehicle user, we use a$Q$-learning algorithm to obtain the optimal path with the target of maximizing the user's utility. The simulation results show that the proposed scheme can bring higher utility to the vehicle users than the conventional schemes.