CCS-24
[Code & Data] |
ReSym: Harnessing LLMs to Recover Variable and Data Structure Symbols from Stripped Binaries. In the proceedings of the ACM Conference on Computer and Communications Security, October 2024. Salt Lake City, USA. |
ISSTA-23
[Code & Data] |
How Effective are Neural Networks for Fixing Security Vulnerabilities? In the proceedings of ACM SIGSOFT International Symposium on Software Testing and Analysis. July 2023. Seattle, USA. Acceptance Rate: 23% (49/215) |
ICSE-23
[Code & Data] |
Impact of Code Language Models on Automated Program Repair. In the proceedings of the International Conference on Software Engineering. May 2023. Melbourne, Australia. Acceptance Rate: 26% (208/796) |
ICSE-23
[Code & Data] |
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. In the proceedings of the International Conference on Software Engineering. May 2023. Melbourne, Australia. Acceptance Rate: 26% (208/796) |
AAAI-23
[Code & Data] |
DisGUIDE: Disagreement-Guided Data-Free Model Extraction. (Oral Presentation) In the proceedings of AAAI Conference on Artificial Intelligence. February, 2023. Washington D.C., USA. Acceptance Rate: 19.6% |
ICSE-22
[Code & Data] |
EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries. In the proceedings of the International Conference on Software Engineering. May 2022. Pittsburgh, USA. Acceptance Rate: 26% (197/751) |
ISSTA-22
[Code & Data] |
DocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions. In the proceedings of ACM SIGSOFT International Symposium on Software Testing and Analysis. July 2022. Virtual. Acceptance Rate: 24% (61/250) |
NeurIPS-21
[Code & Data] |
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. To appear in the proceedings of the Conference on Neural Information Processing Systems, December 2021. Virtual. Acceptance Rate: 26% |
ASE-21
(Tool)
[Code & Data] |
DEVIATE: A Deep Learning Variance Testing Framework. In the proceedings of the IEEE/ACM International Conference on Automated Software Engineering, November, 2021. Virtual/Melbourne, Australia. |
ICSE-21
[Code&Data] |
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. In the proceedings of the International Conference on Software Engineering. May 2021. Acceptance Rate: 22% (138/615) |
ASE-20
[Code&Data] |
Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. In the proceedings of the IEEE/ACM International Conference on Automated Software Engineering, September, 2020. Virtual/Melbourne, Australia. Acceptance Rate: 22.5% (93/414) Won ACM SIGSOFT Distinguished Paper Award! |
ISSTA-20
[Code & Data] |
CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair. In the proceedings of ACM SIGSOFT International Symposium on Software Testing and Analysis. July 2020. Virtual/Los Angeles, United States. Acceptance Rate: 26.5% (43/162) |
FSE-18/EMSE-18
[Data] (Journal First) |
On the Correctness of Electronic Documents: Studying, Finding, and Localizing Inconsistency Bugs in PDF Readers and Files. (Open Access) Accepted to the Springer Empirical Software Engineering. (34 pages) |
FSE-17
[Data] |
QTEP: Quality-aware Test Case Prioritization. In the Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering. Acceptance Rate: 24% (72/295) |
TSE-17
(Journal)
[Data]
|
Measuring the Impact of Code Dependencies on Software Architecture Recovery Techniques. In IEEE Transactions on Software Engineering. |
ICSE-15
(SEIP)
[Data] |
Comparing Software Architecture Recovery Techniques Using Accurate Dependencies. In the proceedings of the International Conference on Software Engineering. Acceptance Rate: 22.5% (23/102) |
SANER-15
[Code & Data] |
CloCom: Mining Existing Source Code for Automatic Comment Generation. In the proceedings of the IEEE International Conference on Software Analysis, Evolution, and Reengineering. (10 pages) Acceptance Rate: 31.9% (46/144) |
EMSE-14
(Journal)
[Data] |
SWordNet: Inferring Semantically Related Words from Software Context. In the Springer Empirical Software Engineering. (28 pages) [DOI] [BIBTEX] |
ASE-13 [Data] |
AutoComment: Mining Question and Answer Sites for Automatic Comment Generation. In the proceedings of the IEEE/ACM International Conference on Automated Software Engineering, New Idea Papers. (6 pages) Acceptance Rate: 23% (74/317) [BIBTEX] |
ICST-12
[Code & Data] |
@tComment: Testing Javadoc Comments to Detect Comment-Code Inconsistencies. In the proceedings of the 5th International Conference on Software Testing, Verification and Validation. April, 2012. (10 pages) Acceptance Rate: 26.9% (39/145). [Slides in PDF] [BIBTEX] |
ICSE-09
[Code & Data] |
Listening to Programmers - Taxonomies and Characteristics of Comments in Operating System Code. In the proceedings of the International Conference on Software Engineering. May, 2009. (11 pages) Acceptance Rate: 12.3% (50/405). [PS] [Slides in PDF] [BIBTEX] |