Code vulnerability detection (CVD) is a critical approach to ensuring the security, stability, and reliability of software. When exploited by malicious actors or hackers, code vulnerabilities can lead to a series of severe consequences and cause significant losses. However, the effectiveness of real-world CVD is currently unsatisfactory, with several issues that need to be optimized and resolved. These issues include the poor quality of real-world CVD datasets, the high resource consumption of code intermediate structures, the imbalance between positive and negative samples, and insufficient feature modeling of code vulnerabilities. To address these challenges, we propose a comprehensive and efficient framework for real-world CVD called MARCOVul. It offers a complete process from data preprocessing to final vulnerability detection, optimizing the entire real-world CVD pipeline. Our approach begins with a data derivation technique which seeks to improve the overall quality of the dataset. Next, we propose a code-specific data augmentation method to tackle the issue of sample imbalance in the dataset. We then propose a code intermediate structure simplification method to reduce computational complexity and resource consumption while fully leveraging the power of language models. Finally, we propose a real-world CVD method based on multi-feature fusion to identify potential security vulnerabilities in the code. Experiments on a large-scale real-world CVD dataset demonstrate the effectiveness of MARCOVul in detecting real-world code vulnerabilities, achieving up to 12.75% BF1 and 6.98% MCC improvements over the best unweighted baselines, and 1.55% BF1 and 1.42% MCC gains over the best weighted ones.

Microservice architecture has been widely adopted for large-scale applications because of its benefits of scalability, flexibility, and reliability. However, due to the heterogeneity of system architecture and variable invocations between services, it is difficult to accurately diagnose the root causes of performance degradation in time. This paper proposes WinG, a system to pinpoint root causes. Firstly, since the characteristic of a system element is difficult to capture due to variable invocations between elements, WinG characterizes an element's status by a feature vector that includes all its invocation relationships. Secondly, WinG adopts a warping procedure to assess an element's anomaly severity based on its status deviation, to mitigate the interference of variable invocations. Thirdly, WinG groups heterogeneous elements with similar invocation characteristics to avoid the interference of diverse elements types. Finally, false alarms are filtered by the anomaly duration and frequency. Experimental evaluation results on the public dataset show that, with the above four methods, WinG can locate root causes with 87% precision, outperforming baseline methods. On average of 78 test cases, it achieves 34% precision improvement over the champion method of the competition.