We aim to propose a system repairing programs with logic errors to be functionally correct among different programming languages. Logic error program repair has always been a thorny problem: First, a logic error is usually harder to repair than a syntax error in a program because it has no diagnostic feedback from compilers. Second, it requires inferring in different ranges (i.e., the distance of related code lines) and tracking symbols across its pseudocode, source code, and test cases. Third, the logic error datasets are scarce, since an ideal logic error dataset should contain lots of components during the development procedure of a program, including a program specification, pseudocode, source code, test cases, and test reports (i.e., test case failure report). In our work, we propose novel solutions to these challenges. First, we introduce pseudocode information to assist logic error localization and correction. We construct a code-pseudocode graph to connect symbols across a source code and its pseudocode and then apply a graph neural network to localize and correct logic errors. Second, we collect logic errors generated in the process of syntax error repairing via DrRepair from 500 programs in the SPoC dataset and reconstruct them to our single logic error dataset, which we leverage to train and evaluate our models. Our experimental results show that we achieve 99.39% localization accuracy and 19.20% full repair accuracy on logic errors with five-fold cross-validation. Based on our current work, we will replenish and construct more complete public logic error datasets and propose a novel system to comprehend different programming languages from several perspectives and correct logic errors to be functionally correct.

In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA). We propose a GRA that recovers a graph's adjacency matrix from the representations via a graph decoder that minimizes the reconstruction loss between the partial graph and the reconstructed graph. We study three types of representations that are trained on the graph, i.e., representations output from graph convolutional network (GCN), graph attention network (GAT), and our proposed simplicial neural network (SNN) via a higher-order combinatorial Laplacian. Unlike the first two types of representations that only encode pairwise relationships, the third type of representation, i.e., SNN outputs, encodes higher-order interactions (e.g., homological features) between nodes. We find that the SNN outputs reveal the lowest privacy-preserving ability to defend the GRA, followed by those of GATs and GCNs, which indicates the importance of building more private representations with higher-order node information that could defend the potential threats, such as GRAs.