The Linux kernel offers nearly 20,000 configuration options, making it highly customizable but also extremely challenging to manually optimize for performance. The diversity of operating environments and workloads further limits the effectiveness of static or expert-crafted configurations. This paper introduces KconfigTune, an automated tuning system that jointly optimizes Linux kernel configuration options and system-level parameters (e.g., procfs entries). We propose extconfig to expand the parameter search space and, for the first time, address configuration dependencies that arise during automated tuning. To ensure valid and bootable kernels, we design a dependency automatic fix tool and a GRUB-based mechanism. KconfigTune models the tuning process as a machine learning problem and applies Bayesian optimization with a random forest model to efficiently explore the vast and interdependent configuration space. Unlike prior approaches that focus solely on either kernel options or system parameters, KconfigTune achieves deeper integration by jointly tuning both compile-time and runtime behaviors. Experimental results show significant performance improvements, with gains of 18.62% and 19.92% over the default configuration in UnixBench and LEBench tests, respectively. Compared to the state-of-the-art, KconfigTune outperforms by 2.9% and 12.69% in these benchmarks. Ablation studies further confirm that these gains primarily stem from the combined tuning of kernel configurations and system-level parameters.

During the operation of high-speed trains, some vehicles will experience wheel polygonization (WP). Severe polygonal wear significantly increases the wheel-rail contact force, reduces the service life of wheelsets, and may cause other component failures. In this article, hierarchical Gaussian Markov chain models were established to estimate the failure time of WP, considering the effects of season, initial wheel radius, and wheel wear. Two-layer hierarchical Gaussian mixture models are employed to analyze the distribution patterns of the failure time of polygonal wheels for motors and trailers in different seasons. The results show that different seasons have a significant impact on the failure time of polygonal wheels, and motors are more prone to polygonal wear. In addition, wheel polygonization tends to occur in pairs. Moreover, when the wheel diameter variation is small, its influence on reliability is insignificant.

Extracting API patterns from the source code has been extensively employed to detect API misuses. However, recent studies manually provide pattern templates as prerequisites, requiring prior software knowledge and limiting their extraction scope. This paper presents APP-Miner (API path pattern miner), a novel static analysis framework for extracting API path patterns via a frequent subgraph mining technique without pattern templates. The critical insight is that API patterns usually consist of APIs’ data-related operations and are commonplace. Therefore, we define API paths as the control flow graphs composed of APIs’ data-related operations, and thereby the maximum frequent subgraphs of the API paths are the probable API path patterns. We implemented APP-Miner and extensively evaluated it on four widely used open-source software: Linux kernel, OpenSSL, FFmpeg, and Apache httpd. We found 116, 35, 3, and 3 new API misuses from the above systems, respectively. Moreover, we gained 19 CVEs.