Program translation aims to convert the input programs from one programming language to another. Automatic program translation is a prized target of software engineering research, which leverages the reusability of projects and improves the efficiency of development. Recently, thanks to the rapid development of deep learning model architectures and the availability of large-scale parallel corpus of programs, the performance of program translation has been greatly improved. However, the existing program translation models are still far from satisfactory, in terms of the quality of translated programs. In this paper, we argue that a major limitation of the current approaches is the lack of consideration of semantic consistency. Beyond lexical consistency, semantic consistency is also critical for the task. To make the program translation model more semantically aware, we propose a general framework named Preserving Semantic Consistency for Program Translation (PSCPT), which considers semantic consistency with regularization in the training objective of program translation and can be easily applied to all encoder-decoder methods with various neural networks (e.g., LSTM, Transformer) as the backbone. We conduct extensive experiments in 7 general programming languages. Experimental results show that with CodeBERT as the backbone, our approach outperforms not only the state-of-the-art open-source models but also the commercial closed large language models (e.g., textdavinci-002, text-davinci-003) on the program translation task. Our replication package (including code, data, etc.) is publicly available at https://github.com/duyali2000/PSCPT.

To alleviate the burden of software maintenance, bug localization, which aims to automatically locate the buggy source files based on the bug report, has drawn significant attention in the software mining community. Recent studies indicate that the program structure in source code carries more semantics reflecting the program behavior, which is beneficial for bug localization. Benefiting from the rich structural information in the Control Flow Graph (CFG), CFG-based bug localization methods have achieved the state-of-the-art performance. Existing CFG-based methods extract the semantic feature from the CFG via the graph neural network. However, the step-wise feature propagation in the graph neural network suffers from the problem of information loss when the propagation distance is long, while the long-distance dependency is rather common in the CFG. In this paper, we argue that the long-distance dependency is crucial for feature extraction from the CFG, and propose a novel bug localization model named sgAttention. In sgAttention, a particularly designed structural-guided attention is employed to globally capture the information in the CFG, where features of irrelevant nodes are masked for each node to facilitate better feature extraction from the CFG. Experimental results on four widely-used open-source software projects indicate that sgAttention averagely improves the state-of-the-art bug localization methods by 32.9\% and 29.2\% and the state-of-the-art pre-trained models by 5.8\% and 4.9\% in terms of MAP and MRR, respectively.

Bug localization aims to automatically locate the buggy source files according to the bug report, which plays an important role in software maintenance. Recent studies indicate that exploiting the program structure is beneficial for bug localization. Benefiting from the rich statement-level implementation detail in the Control Flow Graph (CFG), CFG-based bug localization methods have achieved state-of-the-art performance. However, due to the huge semantic gap between the high-level description of the unexpected program behavior in the bug report and the low-level implementation detail in the CFG, it is challenging to directly establish the match between the bug report and the CFG. In this paper, we argue that this gap can be bridged through the multi-level abstraction of the CFG, where each node in an abstraction level corresponds to a code block with a certain granularity. The multi-level abstraction of the CFG reflects the essence of the structured programming paradigm. We further propose a novel model named MLA (Multi-Level Abstraction of the control flow graph) for bug localization, which contains a particularly designed model that alternately propagates the block feature within and between abstraction levels, corresponding to reflecting the control flow and summarizing the block functionality, respectively. Experimental results on four widely-used open-source software projects show that MLA outperforms the state-of-the-art bug localization methods.