Code review is essential for maintenance of pull request-based software systems. Recommending suitable reviewers for code changes can enhance defect detection and knowledge dissemination. Despite extensive research, the inherent complexity of pull requests (PRs) and reviewer profiles continues to cause challenge for accurate matching them together. Furthermore, existing methods often amplify gender and racial/ethnic disparities due to the lack of attention to biases present in historical review records. To address these issues, we first collected a dataset from 4 large-scale open-source projects involving 50 -month revision history, reaching up to 30 attributes. This dataset includes gender and racial/ethnic information, which was inferred, validated, and incorporated to enable comprehensive data bias analysis in reviewer recommendation tasks. Additionally, we introduce a fairness-aware and accurate approach: CoReBM, which leverages the advanced semantic understanding capabilities of Large Language Models (LLMs) to comprehensively capture the nuanced textual context of both PRs and reviewers, utilizing the robust planning, collaborative, and decision-making abilities of multi-agent systems. CoReBM integrates diverse factors to improve recommendation performance while mitigating bias effects through the incorporation of candidates' gender and racial/ethnic attributes. We evaluate the effectiveness of our approach on this dataset, and the results demonstrate that CoReBM outperforms state-of-the-art methods in both accuracy and fairness in recommendation.

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.

Extract Class refactoring is essential for decomposing large, complex classes to improve code maintainability and readability. Traditional refactoring tools rely heavily on metrics like cohesion and coupling, which often require expert judgment and are not universally applicable. This research proposes a novel approach leveraging deep class property graphs and advanced graph neural networks to automate the identification of refactoring opportunities. By integrating deep semantic properties and fine-grained structural dependencies, this method aims to reduce reliance on expert knowledge and improve the precision and adaptability of refactoring suggestions. Future work will explore hypergraph learning to capture more complex code relationships, further enhancing the proposed method's effectiveness.

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.

Assigning pull requests to appropriate code reviewers can accelerate the review process and help uncover potential bugs. However, the inherent complexities in pull requests and code reviewers present challenges in making suitable matches between them. Prior studies focus on mining rich semantic information from pull requests or profile information from code reviewers to improve efficiency. These approaches often overlook the intrinsic relationships between pull requests and code reviewers, which can be represented by a combination of multiple factors and strategies, resulting in suboptimal recommendation accuracy.

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

As a sub-class of internet of things (IoTs), wireless sensor networks (WSNs) are becoming ubiquitous in recent years, which makes the efficient coverage of sensors challenging. Traditionally, WSNs are composed of omni-directional sensors, which, however, are still limited to unadjustable sensing angle and superfluous energy consumption. Fortunately, these limitations can be overcome by deploying directional sensors in WSNs, thus forming directional sensor networks, namely DSNs. Therefore, it is necessary to propose efficient coverage optimization methods for DSNs to solve the minimum exposure path (MEP) problem that refers to a path along which the intruder can go through WSNs with lowest detection probability. In this paper, a novel MEP-PSO algorithm-based coverage optimization mechanism is proposed to improve the coverage quality in DSNs. With our coverage optimization mechanism, the traditional MEP problem is analyzed by means of discrete geometric theories while the path searching performance is improved based on the particle swarm optimization (PSO) algorithm. Specifically, the deployment scenario is firstly discretized into multiple square grids with uniform sizes. The weighted undirected graph is thus constructed in which the path segment exposure of MEP can be analyzed by discrete geometric theory. Based on the analysis, the feasibility of PSO is evaluated and enhanced in terms of MEP searching. Using our algorithm, the coverage performance of DSNs can be improved significantly by dynamically adjusting the positions of directional sensors. Finally, we conduct extensive experiments to validate the effectiveness of our work.