Automated program repair (APR) has been gaining ground recently. However, a significant challenge that still remains is test overfitting, in which APR-generated patches plausibly pass the validation test suite but fail to generalize. A common practice to assess the correctness of APR-generated patches is to judge whether they are equivalent to ground truth, i.e., developer-written patches, by either generating additional test cases or employing human manual inspections. The former often requires the generation of at least one test that shows behavioral differences between the APR-patched and developer-patched programs. Searching for this test, however, can be difficult as the search space can be enormous. Meanwhile, the latter is prone to human biases and requires repetitive and expensive manual effort. In this paper, we propose a novel technique,Invalidator, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning.Invalidatorleverages program invariants to reason about program semantics while also capturing program syntax through language semantics learned from a large code corpus using a pre-trained language model. Given a buggy program and the developer-patched program,Invalidatorinfers likely invariants on both programs. Then,Invalidatordetermines that an APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains erroneous behaviors from the original buggy program. In case our approach fails to determine an overfitting patch based on invariants,Invalidatorutilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit ofInvalidatoris threefold. First,Invalidatorleverages both semantic and syntactic reasoning to enhance its discriminative capability. Second,Invalidatordoes not require new test cases to be generated, but instead only relies on the current test suite and uses invariant inference to generalize program behaviors. Third,Invalidatoris fully automated. We conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show thatInvalidatorcorrectly classified 79% of overfitting patches, accounting for 23% more overfitting patches being detected than the best baseline.Invalidatoralso substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

Fault localization has been used to provide feedback for incorrect student programs since locations of faults can be a valuable hint for students about what caused their programs to crash. Unfortunately, existing fault localization techniques for student programs are limited because they usually consider either the program's syntax or semantics alone. This motivates the new design of fault localization techniques that use both semantic and syntactical information of the program.In this paper, we introduce FFL (Fine grained Fault Localization), a novel technique using syntactic and semantic reasoning for localizing bugs in student programs. The novelty in FFL that allows it to capture both syntactic and semantic of a program is three-fold: (1) A fine-grained graph-based representation of a program that is adaptive for statement-level fault localization; (2) an effective and efficient model to leverage the designed representation for fault-localization task and (3) a node-level training objective that allows deep learning model to learn from fine-grained syntactic patterns. We compare FFL's effectiveness with state-of-the-art fault localization techniques for student programs (NBL, Tarantula, Ochiai and DStar) on two real-world datasets: Prutor and Codeflaws. Experimental results show that FFL successfully localizes bug for 84.6% out of 2136 programs on Prutor and 83.1% out of 780 programs on Codeflaws concerning the top-10 suspicious statements. FFL also remarkably outperforms the best baselines by 197%, 104%, 70%, 22% on Codeflaws dataset and 10%, 17%, 15% and 8% on Prutor dataset, in term of top-1, top-3, top-5, top-10, respectively.