Machine Learning
Identifying Reasons for Software Changes Using Historic Databases
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
An Information Retrieval Approach to Concept Location in Source Code
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Proceedings of the 28th international conference on Software engineering
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Understanding collateral evolution in Linux device drivers
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Predicting Faults from Cached History
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Detection of Duplicate Defect Reports Using Natural Language Processing
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Extraction of bug localization benchmarks from history
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Documenting and automating collateral evolutions in linux device drivers
Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
Introduction to Information Retrieval
Introduction to Information Retrieval
Source Code Retrieval for Bug Localization Using Latent Dirichlet Allocation
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Is it a bug or an enhancement?: a text-based approach to classify change requests
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Fair and balanced?: bias in bug-fix datasets
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Extracting paraphrases of technical terms from noisy parallel software corpora
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Inferring Resource Specifications from Natural Language API Documentation
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
A discriminative model approach for accurate duplicate bug report retrieval
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Recurring bug fixes in object-oriented programs
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Semi-Supervised Learning
LINKSTER: enabling efficient manual inspection and annotation of mined data
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Detecting Duplicate Bug Report Using Character N-Gram-Based Features
APSEC '10 Proceedings of the 2010 Asia Pacific Software Engineering Conference
Faults in linux: ten years later
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
Text Mining Support for Software Requirements: Traceability Assurance
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Proceedings of the 8th Working Conference on Mining Software Repositories
aComment: mining annotations from comments and code to detect interrupt related concurrency bugs
Proceedings of the 33rd International Conference on Software Engineering
ReLink: recovering links between bugs and changes
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Application of swarm techniques to requirements tracing
Requirements Engineering - Special Issue on Best Papers of RE'10: Requirements Engineering in a Multi-faceted World
Towards more accurate retrieval of duplicate bug reports
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Finding relevant answers in software forums
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Automatic patch generation learned from human-written patches
Proceedings of the 2013 International Conference on Software Engineering
Will my patch make it? and how fast?: case study on the Linux kernel
Proceedings of the 10th Working Conference on Mining Software Repositories
Hi-index | 0.00 |
In the evolution of an operating system there is a continuing tension between the need to develop and test new features, and the need to provide a stable and secure execution environment to users. A compromise, adopted by the developers of the Linux kernel, is to release new versions, including bug fixes and new features, frequently, while maintaining some older "longterm" versions. This strategy raises the problem of how to identify bug fixing patches that are submitted to the current version but should be applied to the longterm versions as well. The current approach is to rely on the individual subsystem maintainers to forward patches that seem relevant to the maintainers of the longterm kernels. The reactivity and diligence of the maintainers, however, varies, and thus many important patches could be missed by this approach. In this paper, we propose an approach that automatically identifies bug fixing patches based on the changes and commit messages recorded in code repositories. We compare our approach with the keyword-based approach for identifying bug-fixing patches used in the literature, in the context of the Linux kernel. The results show that our approach can achieve a 53.19% improvement in recall as compared to keyword-based approaches, with similar precision.