ABSTRACT Automated program repair (APR) aims to automatically detect and fix software defects, thereby improving software reliability and reducing debugging effort. Recently, researchers have explored the retrieval augmentation techniques to enhance large code models' performance in program repair. Existing retrieval augmentation models often inject retrieved information at the input layer, which can lead to input sequence inflation and interfere with the encoder's ability to focus on the core repair task. Meanwhile, learning‐based methods frequently produce unreliable patches, lacking mechanisms to verify or refine low‐confidence outputs during generation. To address these challenges, this paper proposes RADI‐PR, a novel approach that integrates retrieval augmentation and decoding intervention at the model's output layer. RADI‐PR dynamically incorporates relevant repair patterns based on historical fixes and intervenes in low‐confidence generations to enhance both the accuracy and reliability of generated patches. Comprehensive evaluations on four benchmark datasets, including Java, FixJS, Codeflaws and TSSB‐3 M, show that RADI‐PR consistently outperforms baseline methods. RADI‐PR achieves improvements of up to 5.5% in Precision, 3.2% in F1‐score and 2.2% in Accuracy.

Software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems.In existing code-visualization-based defect prediction methods, challenges arise from mixing code information and the potential omission of critical defect features.To enhance the completeness of code features, this paper proposes a defect prediction model based on code grayscale pixel visualization with a fusion attention mechanism (Gpv2DP).Gpv2DP converts code into grayscale images and reshapes the images to a standard size, effectively alleviating the information loss problem caused by element mixing and image cropping.Furthermore, it constructs a code feature extracting network that simultaneously integrates the channel, spatial and 3D attention.We conduct empirical experiments on ten open-source Java projects from the PROMISE repository.The results show that the F-measure and AUC metrics of Gpv2DP outperform related defect prediction methods.

Code clone detection technology aims to automatically detect code similarity and help developers identify and reduce code duplication.While code syntax analysis-based methods are commonly used for clone detection, they may not capture semantic information due to bypassing the analysis of code text.To address this issue, this paper proposes a new method called visualization representation learning for code clone detection (VRL4CCD).This method converts source code fragments into grayscale images to preserve textual information and then utilizes VGG16 and a self-attention mechanism to extract features related to code semantic similarity.A siamese neural network is used to learn the similarity pattern between code features.Experimental results on the Big Clone Bench and Google Code Jam datasets demonstrate that VRL4CCD outperforms current clone detection methods regarding precision, recall, and F1-score, indicating the effectiveness of code visualization technology in clone detection tasks.

Defect prediction technology helps software quality assurance teams understand the distribution of software defects, which can assist them to allocate testing and verification resources appropriately. Current visualization-based software defect prediction methods lack spatial and global information of code images during the feature extraction process. To solve the problem of incomplete information, this paper proposes a Convolutional Neural Network with Global Self-Attention (CNN-GSA). The method converts codes into corresponding images and uses an improved convolutional neural network, which combines channel attention, spatial attention, and self-attention mechanisms in a global attention layer, to extract defect-related structural and semantic features in code images. Empirical study shows that the model built with the features generated by CNN-GSA can achieve better F-measure results in defect prediction tasks.