added PreciseBugCollector

_bibliography/publications.bib

@@ -3,6 +3,23 @@
 References
 ==========
 
+@INPROCEEDINGS {Ye2023PreciseBugCollector,
+author = {Y. He and Z. Chen and C. Le Goues},
+booktitle = {2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)},
+title = {PreciseBugCollector: Extensible, Executable and Precise Bug-Fix Collection: Solution for Challenge 8: Automating Precise Data Collection for Code Snippets with Bugs, Fixes, Locations, and Types},
+year = {2023},
+pages = {1899-1910},
+abstract = {Bug datasets are vital for enabling deep learning techniques to address software maintenance tasks related to bugs. However, existing bug datasets suffer from precise and scale limitations: they are either small-scale but precise with manual validation or large-scale but imprecise with simple commit message processing. In this paper, we introduce Precise-BugCollector, a precise, multi -language bug collection approach that overcomes these two limitations. PreciseBugCollector is based on two novel components: a) A bug tracker to map the codebase repositories with external bug repositories to trace bug type information, and b) A bug injector to generate project-specific bugs by injecting noise into the correct codebases and then executing them against their test suites to obtain test failure messages. We implement PreciseBugCollector against three sources: 1) A bug tracker that links to the national vulnerability data set (NVD) to collect general-wise vulnerabilities, 2) A bug tracker that links to OSS-Fuzz to collect general-wise bugs, and 3) A bug injector based on 16 injection rules to generate project-wise bugs. To date, PreciseBugCollector comprises 1057818 bugs extracted from 2968 open-source projects. Of these, 12602 bugs are sourced from bug repositories (NVD and OSS-Fuzz), while the remaining 1045216 project-specific bugs are generated by the bug injector. Considering the challenge objectives, we argue that a bug injection approach is highly valuable for the industrial setting, since project-specific bugs align with domain knowledge, share the same codebase, and adhere to the coding style employed in industrial projects.},
+keywords = {industries;deep learning;training;location awareness;software maintenance;computer bugs;manuals},
+doi = {10.1109/ASE56229.2023.00163},
+url = {https://doi.ieeecomputersociety.org/10.1109/ASE56229.2023.00163},
+publisher = {IEEE Computer Society},
+address = {Los Alamitos, CA, USA},
+month = {sep}
+}
+
+
+
 @inproceedings{Duerschmid2024ROSInfer,
   author = {D\"{u}rschmid, Tobias and Timperley, Christopher Steven and Garlan, David and {Le~Goues}, Claire},
   title = {ROSInfer: Statically Inferring Behavioral Component Models for ROS-based Robotics Systems},