Automatic software repair: A bibliography
M Monperrus - ACM Computing Surveys (CSUR), 2018 - dl.acm.org
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 …
consists of automatically finding a solution to software bugs without human intervention. This …
Unsupervised translation of programming languages
B Roziere, MA Lachaux… - Advances in neural …, 2020 - proceedings.neurips.cc
A transcompiler, also known as source-to-source translator, is a system that converts source
code from a high-level programming language (such as C++ or Python) to another …
code from a high-level programming language (such as C++ or Python) to another …
Deepbugs: A learning approach to name-based bug detection
Natural language elements in source code, eg, the names of variables and functions,
convey useful information. However, most existing bug detection tools ignore this …
convey useful information. However, most existing bug detection tools ignore this …
Getafix: Learning to fix bugs automatically
Static analyzers help find bugs early by warning about recurring bug categories. While fixing
these bugs still remains a mostly manual task in practice, we observe that fixes for a specific …
these bugs still remains a mostly manual task in practice, we observe that fixes for a specific …
A new era in software security: Towards self-healing software via large language models and formal verification
This paper introduces an innovative approach that combines Large Language Models
(LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially …
(LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially …
Lessons from building static analysis tools at google
C Sadowski, E Aftandilian, A Eagle… - Communications of the …, 2018 - dl.acm.org
Lessons from building static analysis tools at Google Page 1 58 COMMUNICATIONS OF THE
ACM | APRIL 2018 | VOL. 61 | NO. 4 Lessons from Building Static Analysis Tools at Google …
ACM | APRIL 2018 | VOL. 61 | NO. 4 Lessons from Building Static Analysis Tools at Google …
Deeplinedp: Towards a deep learning approach for line-level defect prediction
C Pornprasit… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …
Predicting defective lines using a model-agnostic technique
S Wattanakriengkrai, P Thongtanunam… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Defect prediction models are proposed to help a team prioritize the areas of source code
files that need Software Quality Assurance (SQA) based on the likelihood of having defects …
files that need Software Quality Assurance (SQA) based on the likelihood of having defects …
Tricorder: Building a program analysis ecosystem
C Sadowski, J Van Gogh, C Jaspan… - 2015 IEEE/ACM 37th …, 2015 - ieeexplore.ieee.org
Static analysis tools help developers find bugs, improve code readability, and ensure
consistent style across a project. However, these tools can be difficult to smoothly integrate …
consistent style across a project. However, these tools can be difficult to smoothly integrate …
Understanding software-2.0: A study of machine learning library usage and evolution
Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using
learned models, ie, Software-2.0, has gained substantial adoption. However, we do not …
learned models, ie, Software-2.0, has gained substantial adoption. However, we do not …