Papers Program Repair

KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair

Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from ex…

Conversational Automated Program Repair

Automated Program Repair (APR) can help developers automatically generate patches for bugs. Due to the impressive performance obtained using Large Pre-Trained Language Models (LLMs) on many code related tasks, researcher…

Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning

Automated program repair (APR) faces the challenge of test overfitting, where generated patches pass validation tests but fail to generalize. Existing methods for patch assessment involve generating new tests or manual i…

Improving Automated Program Repair with Domain Adaptation

Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool. APR tools have recently experienced promising results by leveraging state-of-the-art Neural Langua…

Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5

Automated software debugging is a crucial task for improving the productivity of software developers. Many neural-based techniques have been proven effective for debugging-related tasks such as bug localization and progr…

Repairing Bugs in Python Assignments Using Large Language Models

Students often make mistakes on their introductory programming assignments as part of their learning process. Unfortunately, providing custom repairs for these mistakes can require a substantial amount of time and effort…

Repair Is Nearly Generation: Multilingual Program Repair with LLMs

Most programmers make mistakes when writing code. Some of these mistakes are small and require few edits to the original program -- a class of errors recently termed last mile mistakes. These errors break the flow for ex…

BigIssue: A Realistic Bug Localization Benchmark

As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and B…

InvAASTCluster: On Applying Invariant-Based Program Clustering to Introductory Programming Assignments

Due to the vast number of students enrolled in programming courses, there has been an increasing number of automated program repair techniques focused on introductory programming assignments (IPAs). Typically, such techn…

C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments

Due to the vast number of students enrolled in Massive Open Online Courses (MOOCs), there has been an increasing number of automated program repair techniques focused on introductory programming assignments (IPAs). Such …

Leveraging Causal Inference for Explainable Automatic Program Repair

Deep learning models have made significant progress in automatic program repair. However, the black-box nature of these methods has restricted their practical applications. To address this challenge, this paper presents …

AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations

In software development, it is common for programmers to copy-paste or port code snippets and then adapt them to their use case. This scenario motivates the code adaptation task -- a variant of program repair which aims …

Neural Program Repair: Systems, Challenges and Solutions

Automated Program Repair (APR) aims to automatically fix bugs in the source code. Recently, as advances in Deep Learning (DL) field, there is a rise of Neural Program Repair (NPR) studies, which formulate APR as a transl…

Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods

Users around the world rely on software-intensive systems in their day-to-day activities. These systems regularly contain bugs and security vulnerabilities. To facilitate bug fixing, data-driven models of automatic progr…

MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning

We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs. Since the previous approaches fix a single error in a line, it is inevitable to iterate the fixing …

GRAPHIX: A Pre-trained Graph Edit Model for Automated Program Repair

We present GRAPHIX, a pre-trained graph edit model for automatically detecting and fixing bugs and code quality issues in Java programs. Unlike sequence-to-sequence models, GRAPHIX leverages the abstract syntax structure…

Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades

Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have …

Grammar-Based Patches Generation for Automated Program Repair

TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer

The problem of fixing errors in programs has attracted substantial interest over the years. The key challenge for building an effective code fixing tool is to capture a wide range of errors and meanwhile maintain high ac…

Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix

The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program can be transformed to a translation task. …