arXiv subjects

Shangwei Lin

Publications and source records attributed to Shangwei Lin.

3 recordsLinked to original sources

arXiv2025

Completely independent Steiner trees and corresponding tree connectivity

The $S$-Steiner tree packing problem provides mathematical foundations for optimizing multi-path information transmission, particularly in designing fault-tolerant parallelized routing architectures for massive-scale network infrastructures. In this article, we propose the definitions of completely independent $S$-Steiner trees (CISSTs for short) and generalized $k^*$-connectivity, which generalize the definitions of internally disjoint $S$-Steiner trees and generalized $k$-connectivity. Given a connected graph $G = (V,E)$ and a vertex subset $S\subseteq V, |S|\geq 2,$ an $S$-Steiner tree of $G$ is a subtree in $G$ that spans all nodes in $S.$ The $S$-Steiner trees $T_1,T_2,\cdots, T_k$ of $G$ are completely independent pairwise if for any $1\leq p<q\leq k,$ $E(T_p)\cap E(T_q)=\emptyset$ , $V(T_p)\cap V(T_q)=S,$ and for any two vertices $x_{1},x_{2}$ in $S$, the paths connecting $x_{1}$ and $x_{2}$ in $T_p,T_q$ are pairwise internally disjoint. The packing number of CISSTs, denoted by $\kappa^*_G(S),$ is the maximum number of CISSTs in $G.$ The generalized $k^*$-connectivity $\kappa_k^*(G)$ is the minimum $\kappa_G^*(S)$ for $S$ ranges over all $k$-subsets of $V(G).$ We provide a detailed characterization of CISSTs. Also, we investigate the CISSTs of complete graphs and complete bipartite graphs. Furthermore, we determine the generalized $k^*$-connectivity for complete graphs and give a tight lower bound of the generalized $k^*$-connectivity for complete bipartite graphs.

math.CO↗

arXiv2024

Pre-trained Model-based Actionable Warning Identification: A Feasibility Study

Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly learn an AWI classifier from labeled warnings, are notably common. However, these approaches still face the problem of restricted performance due to the direct reliance on a limited number of labeled warnings to develop a classifier. Very recently, Pre-Trained Models (PTMs), which have been trained through billions of text/code tokens and demonstrated substantial success applications on various code-related tasks, could potentially circumvent the above problem. Nevertheless, the performance of PTMs on AWI has not been systematically investigated, leaving a gap in understanding their pros and cons. In this paper, we are the first to explore the feasibility of applying various PTMs for AWI. By conducting the extensive evaluation on 10K+ SpotBugs warnings from 10 large-scale and open-source projects, we observe that all studied PTMs are consistently 9.85%~21.12% better than the state-of-the-art ML-based AWI approaches. Besides, we investigate the impact of three primary aspects (i.e., data preprocessing, model training, and model prediction) in the typical PTM-based AWI workflow. Further, we identify the reasons for current PTMs' underperformance on AWI. Based on our findings, we provide several practical guidelines to enhance PTM-based AWI in future work.

cs.SE↗

arXiv2023

Machine Learning for Actionable Warning Identification: A Comprehensive Survey

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based AWI approaches, benefiting from ML's strong ability to learn subtle and previously unseen patterns from historical data, have demonstrated superior performance. However, a comprehensive overview of these approaches is missing, which could hinder researchers/practitioners from understanding the current process and discovering potential for future improvement in the ML-based AWI community. In this paper, we systematically review the state-of-the-art ML-based AWI approaches. First, we employ a meticulous survey methodology and gather 51 primary studies from 2000/01/01 to 2023/09/01. Then, we outline the typical ML-based AWI workflow, including warning dataset preparation, preprocessing, AWI model construction, and evaluation stages. In such a workflow, we categorize ML-based AWI approaches based on the warning output format. Besides, we analyze the techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future ML-based AWI approaches, focusing on aspects like data improvement (e.g., enhancing the warning labeling strategy) and model exploration (e.g., exploring large language models for AWI).

cs.SE↗