PraPR: Practical Program Repair via Bytecode Mutation Automated program repair (APR) is one of the recent advances in automated software engineering aiming for reducing the burden of debugging by suggesting highquality patches that either directly fix the bugs, or help the programmers in the course of manual debugging. We believe scalability, applicability, and accurate patch validation are the main design objectives for a practical APR technique. In this paper, we present PraPR, our implementation of a practical APR technique that operates at the level of JVM bytecode. We discuss design decisions made in the development of PraPR, and argue that the technique is a viable baseline toward attaining aforementioned objectives. Our experimental results show that: (1) PraPR can fix more bugs than stateoftheart APR techniques and can be over 10X faster, (2) stateoftheart APR techniques suffer from dataset overfitting, while the simplistic templatebased PraPR performs more consistently on different datasets, and (3) PraPR can fix bugs for other JVM languages, such as Kotlin. PraPR is publicly available at https:github.compraprprapr.

Improving Patch Quality by Enhancing Key Components of Automatic Program Repair The error repair process in software systems is, historically, a resourceconsuming task that relies heavily in developer manual effort. Automatic program repair approaches enable the repair of software with minimum human interaction, therefore, mitigating the burden from developers. However, a problem automatically generated patches commonly suffer is generating lowquality patches (which overfit to one program specification, thus not generalizing to an independent oracle evaluation). This work proposes a set of mechanisms to increase the quality of plausible patches including an analysis of test suite behavior and their key characteristics for automatic program repair, analyzing developer behavior to inform the mutation operator selection distribution, and a study of patch diversity as a means to create consolidated higher quality fixes.