Code-based Automated Program Fixing

Many programmers, when they encounter an error, would like to have the benefit of automatic fix suggestions---as long as they are, most of the time, adequate. Initial research in this direction has generally limited itself to specific areas, such as data structure classes with carefully designed interfaces, and relied on simple approaches. To provide high-quality fix suggestions in a broad area of applicability, the present work relies on the presence of contracts in the code, and on the availability of dynamic analysis to gather evidence on the values taken by expressions derived from the program text. The ideas have been built into the AutoFix-E2 automatic fix generator. Applications of AutoFix-E2 to general-purpose software, such as a library to manipulate documents, show that the approach provides an improvement over previous techniques, in particular purely model-based approaches

Mining Repair Actions for Automated Program Fixing

National audienceAutomated program fixing consists of generating source code in order to fix bugs in an automated manner. Our intuition is that automated program fixing can imitate human-based program fixing. Hence, we present a method to mine repair actions from software repositories. A repair action is a small semantic modification on code such as adding a method call. A repair model can be defined as a set of repair action. By applying our method on 14 repositories of Java software and 89993 versioning transactions, we present two repair models that are meant to be generalizable and reusable for automated program fixing. Hence, we then show how those repair actions can be used in an automated software repair process called MCRepair

Mining Repair Actions for Guiding Automated Program Fixing

Automated program fixing consists of generating source code in order to fix bugs in an automated manner. Our intuition is that automated program fixing can imitate human-based program fixing. Hence, we present a method to mine repair actions from software repositories. A repair action is a small semantic modification on code such as adding a method call. We then decorate repair actions with a probability distribution also learnt from software repositories. Our probabilistic repair models enable us to mathematically reason on the automated software repair process. By applying our method on 14 repositories of Java software and 89993 versioning transactions, we show that our probabilistic repair actions are able to guide the automated fixing process in the repair space, with a probabilistic focus on likely repair shapes first

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}

Evaluating the Robustness of Deep Learning Models on Automated Program Repair

Automated Program Repair (APR) helps improve the efficiency of software development and maintenance. In recent years, Deep Learning (DL) approaches have been applied to the APR field and have shown promising potential in fixing software bugs automatically. The DL-based APR models translate buggy code to correct code directly. Some recent works test the general performance of various deep learning models on downstream tasks, e.g., code search and method name prediction. However, there still needs to be a fair evaluation of the deep learning models on automated program repair. This paper aims to quantitatively and comparatively evaluate the repair performance and robustness of DL-based APR models. We first fine-tune seven pre-trained models and train two models from scratch on the unified dataset for a fair comparison of repair performance. Then, we conduct a robustness evaluation for nine trained models above against nine semantic-preserving code transformations. Our experiments show that DL-based APR models with pre-training perform better repair performance and robustness than those trained from scratch. Additionally, most APR models fine-tuned on the concrete code datasets have better repair performance than those fine-tuned on the abstract code datasets. Furthermore, most encoder-decoder-based and decoder-based APR models have better repair accuracy than encoder-based ones. Finally, compared with renaming-related code transformations, semantic-preserving transformations related to the change of syntactic structure have a more significant impact on the repair robustness of DL-based APR models. The results provide useful insights for achieving better DL-based APR approaches. Index Terms–automated program repair, deep learning, robustness testin

Automated Fixing of Programs with Contracts

This paper describes AutoFix, an automatic debugging technique that can fix faults in general-purpose software. To provide high-quality fix suggestions and to enable automation of the whole debugging process, AutoFix relies on the presence of simple specification elements in the form of contracts (such as pre- and postconditions). Using contracts enhances the precision of dynamic analysis techniques for fault detection and localization, and for validating fixes. The only required user input to the AutoFix supporting tool is then a faulty program annotated with contracts; the tool produces a collection of validated fixes for the fault ranked according to an estimate of their suitability. In an extensive experimental evaluation, we applied AutoFix to over 200 faults in four code bases of different maturity and quality (of implementation and of contracts). AutoFix successfully fixed 42% of the faults, producing, in the majority of cases, corrections of quality comparable to those competent programmers would write; the used computational resources were modest, with an average time per fix below 20 minutes on commodity hardware. These figures compare favorably to the state of the art in automated program fixing, and demonstrate that the AutoFix approach is successfully applicable to reduce the debugging burden in real-world scenarios.Comment: Minor changes after proofreadin

Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature