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Automated Program Repair (APR) is a technique that can automatically fix software defects without manual debugging, playing a crucial role in software development and maintenance. However, the patches generated by APR still suffer from the problem of overfitting, which poses a significant threat to practical applications. Previous studies have proposed various approaches to predict the correctness of the patch to address this issue, primarily including static-based methods, dynamic-based methods, and learning-based methods. However, these methods all have their own limitations, making it difficult to accurately extract code semantic features and achieve comprehensive prediction. To address the aforementioned challenges, we propose a learning-based unsupervised classification model, A utomated P atch c O rrectness a S sessmen T based on mu L tiple p E rspectives (APOSTLE), for predicting the correctness of patches. Specifically, APOSTLE consists of three components: code vectorization component, where advanced pre-trained models are used to achieve efficient extraction of semantic features from the code; similarity and code change degree calculation component, where APOSTLE calculates the similarity and the degree of change to the code; comprehensive evaluation component, where APOSTLE conducts comprehensive evaluation, addressing the issue of prediction comprehensiveness. Experiments on a collection of 1278 patches (written by developers or generated by 32 APR tools) demonstrate that APOSTLE achieves an AUC value of 0.801, an MAP value of 0.855, and an MRR value of 0.944, outperforming the state-of-the-art approach BATS by 8.3%, 6.0%, and 9.0%, respectively. APOSTLE successfully achieves accurate extraction of code semantic features while achieving comprehensive patch correctness prediction. • APOSTLE is a learning-based unsupervised model with advanced pre-trained models. • APOSTLE solves low code semantic feature extraction during code vectorization process. • APOSTLE achieves competitive performances on large trusted patch dataset in APCA field. [ABSTRACT FROM AUTHOR]

Code vulnerability exposes millions of software to the possibility of being attacked, as evidence every year on increasing reports of security issues, such as information leaks, system compromise, and denial of service. Despite with many vulnerability detection models proposed so far, their effectiveness is still limited due to the ignorance of syntactic structural information analysis in source code and the improper handling of labeling errors. To address these issues, we propose the Graph Confident Learning for Software Vulnerability Detection (GCL4SVD) model, a machine learning model to detect software vulnerability in the development phase. It comprises two components: code graph embedding and graph confident learning denoising. To address the syntactic structural information analysis limitation, the code graph embedding component extracts the structure and semantic information of source code with a sliding window mechanism, and then encodes source code into a graph structure to capture the patterns and characteristics of code vulnerabilities. Additionally, the graph confident learning denoising component identifies labeling errors to improve the quality of training set. Experimental results show that GCL4SVD outperforms the state-of-the-art vulnerability detection models on four open source datasets by 3.7%, 3.3%, 2.5%, 0.8% in terms of Accuracy, respectively, and by 10.2%, 21.8%, 8.2%, 11.2% in terms of F1-score. [ABSTRACT FROM AUTHOR]

Code vulnerabilities are common in software systems and may cause many problems, including Stack Overflow, memory leaks, and so on. Public reports show that code vulnerabilities are increasing year by year, which brings greater threats to the security of software systems. Thus a variety of neural network models have been developed to detect code vulnerabilities. However, the previous neural network models cannot fully express the semantics and structure of the code with as little overhead as possible, and they also cannot enhance learning of difficult samples. Addressing to this issue, we designed a model built upon GGNN for Detecting Software Vulnerabilities (GDSV), which contains three components. Specifically, Graph Embedding component extracts the semantic and structural features, and generates a graph representation of the code; GGNN component learns these features and detects vulnerabilities in the code; weighted component improves the learning ability of Vulnerable samples through the Focal Loss function. A serial of experiments on the datasets of FFmpeg and QEMU were conducted, and the results show that GDSV performs better than the state-of-the-art efforts based on various widely used evaluations. • GDSV detects software vulnerability. • GGNN learns features. • GDSV outperforms other models. [ABSTRACT FROM AUTHOR]

Software code review is a crucial quality assurance procedure for software systems. As a result, some automated code review models have been proposed that jointly consider the reviewer's comments and code. It is worth noting that these previous models have not solved the problem of insufficient diversity of generated code, which can lead to a low accuracy of generated modified code. Therefore, we introduce a method, called SMILER (Structuring Meaningful Code Review), to improve the effectiveness of code review by enhancing the diversity of generated code. Specifically, SMILER consists of two models, where each model consists of four components, i.e., encoder , decoder , prior net and posterior net. The encoder and decoder learn parameters and generate possible code for automating the process of code review. In the prior net and posterior net, Gaussian noise is introduced to increase the diversity of the generated code and improve the performance of the model. Experimental studies on 17,194 code pairs and triplets demonstrate that SMILER outperforms state-of-the-art models from the perspectives of both the reviewer and developer, respectively, in terms of perfect prediction. [ABSTRACT FROM AUTHOR]