Journal ArticleDOI
Automated program repair
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TLDR
This research presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually fixing programming mistakes.Abstract:
Automated program repair can relieve programmers from the burden of manually fixing the ever-increasing number of programming mistakes.read more
Citations
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Proceedings Article
Hoppity: learning graph transformations to detect and fix bugs in programs
TL;DR: A learning-based approach to detect and fix a broad range of bugs in Javascript programs that targets bugs that are more complex and semantic in nature (i.e.~bugs that require adding or deleting statements to fix).
Journal ArticleDOI
Getafix: learning to fix bugs automatically
TL;DR: In this article, the authors present Getafix, an approach that produces human-like fixes while being fast enough to suggest fixes in time proportional to the amount of time needed to obtain static analysis results in the first place.
Proceedings ArticleDOI
Fuzz testing based data augmentation to improve robustness of deep neural networks
TL;DR: This paper proposes a technique that re-purposes software testing methods, specifically mutation-based fuzzing, to augment the training data of DNNs, with the objective of enhancing their robustness, and casts the DNN data augmentation problem as an optimization problem.
Posted Content
Getafix: Learning to fix bugs automatically.
TL;DR: Getafix is the first industrially-deployed automated bug-fixing tool that learns fix patterns from past, human-written fixes to produce human-like fixes and is reported on deploying within Facebook, where it contributes to the reliability of software used by billions of people.
Proceedings ArticleDOI
On the efficiency of test suite based program repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
Kui Liu,Shangwen Wang,Anil Koyuncu,Kisub Kim,Tegawendé F. Bissyandé,Dongsun Kim,Peng Wu,Jacques Klein,Xiaoguang Mao,Yves Le Traon +9 more
TL;DR: A large-scale empirical study on the efficiency, in terms of quantity of generated patch candidates of the 16 open-source repair tools for Java programs, and notes that current template-based repair systems are actually least efficient as they tend to generate majoritarily irrelevant patch candidates.
References
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Journal ArticleDOI
Symbolic execution and program testing
TL;DR: A particular system called EFFIGY which provides symbolic execution for program testing and debugging is described, which interpretively executes programs written in a simple PL/I style programming language.
Proceedings ArticleDOI
Control-flow integrity
TL;DR: Control-Flow Integrity provides a useful foundation for enforcing further security policies, as it is demonstrated with efficient software implementations of a protected shadow call stack and of access control for memory regions.
Proceedings ArticleDOI
Automatically finding patches using genetic programming
TL;DR: A fully automated method for locating and repairing bugs in software that works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices is introduced.
Proceedings ArticleDOI
The essence of command injection attacks in web applications
Zhendong Su,Gary Wassermann +1 more
TL;DR: This paper presents the first formal definition of command injection attacks in the context of web applications, and gives a sound and complete algorithm for preventing them based on context-free grammars and compiler parsing techniques.
Proceedings ArticleDOI
SemFix: program repair via semantic analysis
TL;DR: This paper presents an automated repair method based on symbolic execution, constraint solving and program synthesis, which reports a higher success-rate than genetic programming based repair, and produces a repair faster.