Guanli Yue

Collaborative Software Development (CSD) as an effective software development paradigm has achieved rapid development. In CSD, one critical and challenging problem is to find a group of compatible and diverse developers who can complete the assigned task as quickly as possible. To solve this problem, most previous approaches focus on measuring the relevance of tasks and developers based on their historical interactions. However, such approaches usually suffer from data sparsity and cold start problem which may reduce the recommendation accuracy. Moreover, previous approaches often ignored the diversity of team members which may reduce the overall collaboration efficiency. In this paper, we propose an attentive text-enhanced graph embedding learning framework, termed as CsdRec. We first incorporate text embedding into one-hop interaction learning and two-hop graph learning to learn the relevance between tasks and team members. Then, we explicitly model the team members’ diversity and incorporate it with relevance. Finally, to evaluate the performance of CsdRec, we conduct extensive experiments on two real-world datasets. The experimental results show that CsdRec outperforms other state-of-the-art approaches significantly.

Code review as an effective software quality assurance practice has been widely applied in many open-source software communities. However, finding a suitable reviewer for certain codes can be very challenging in open-source communities due to the difficulty of learning the characteristics of reviewers and the code-reviewer interaction sparsity in open-source software communities. To tackle this problem, most previous approaches focus on learning developers’ capabilities and experiences and recommending suitable developers based on their historical interactions. However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. In this paper, we propose an attentive neighbor embedding propagation enhanced code reviewer recommendation framework (termed ANEP). In ANEP, we first construct the reviewer–code interaction graph and learn the semantic representations of the reviewer and code based on the transformer model. Then, we explicitly explore the attentive high-order embedding propagation of reviewers and code and refine the representations along their neighbors. Finally, to evaluate the effectiveness of ANEP, we conduct extensive experiments on four real-world datasets. The experimental results show that ANEP outperforms other state-of-the-art approaches significantly.

Knowledge reduction is a crucial topic in formal concept analysis. There always exists uncertain, symmetric linguistic-evaluation information in social life, which leads to high complexity in the process of knowledge representation. In order to overcome this problem, we are focused on studying the linguistic-concept-reduction methods in an uncertain environment with fuzzy linguistic information. Based on three-way decisions and an attribute-oriented concept lattice, we construct a fuzzy-object-induced three-way attribute-oriented linguistic (FOEAL) concept lattice, which provides complementary conceptual structures of a three-way concept lattice with symmetric linguistic-evaluation information. Through the granular concept of the FOEAL lattice, we present the corresponding linguistic concept granular consistent set and granular reduction. Then, we further employ the linguistic concept discernibility matrix and discernibility function to calculate the granular reduction set. A similar issue on information entropy is investigated to introduce a method of entropy reduction for the FOEAL lattice, and the relation between the linguistic concept granular reduction and entropy reduction is discussed. The efficiency of the proposed method is depicted by some examples and comparative analysis.

Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters which are challenging to be determined for different data types. This paper proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modelling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

In the density peaks clustering algorithm (DPC), points in high density regions are more likely to be selected as cluster centers, resulting in poor performance on complex datasets that are of various densities. A Density Peaks Clustering Algorithm Based on Density Stratification and Subcluster Fusion (DSSF-DPC) was proposed. The algorithm firstly fits the local density distribution of the points by Gaussian mixture model, and finds the local density change turning points of the points; then, according to the turning points of the density change, the dataset is divided into different density regions, and the density peaks clustering algorithm is used separately in each density region. Finally, the subcluster fusion algorithm is used to obtain the final clustering results. The metrics used are Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI). Experimental results show that our method can recognize clusters regardless of their size, shape and dimension; is robust to noise; and is remarkably superior to DPC, SNN-DPC (Shared-nearest-neighbor-based Clustering by Fast Search and Find of Density Peaks), OPTICS, DBSCAN and K-means.