2024
Journal
- EBRAHIMI, E., ADAMS, B., OLIVA, G.A. and HASSAN, A.E. (2024). A Large-Scale Exploratory Study on the Proxy Pattern in Ethereum, Empirical Software Engineering, Springer, to appear. BibTeX
- ZHAO, Z., CHEN, Y., BANGASH, A.A., ADAMS, B. and HASSAN, A.E. (2024). An Empirical Study of Challenges in Machine Learning Asset Management, Empirical Software Engineering, Springer, to appear. BibTeX
- KAMATH, D.M., FERNANDES, E., ADAMS, B. and HASSAN, A.E. (2024). On Combining Commit Grouping and Build Skip Prediction to Reduce Redundant Continuous Integration Activity, Empirical Software Engineering, Springer, to appear. BibTeX
- MALIK, A., ADAMS, B. and HASSAN, A.E. (2024). Towards Graph-Anonymization of Software Analytics Data: Empirical Study on JIT Defect Prediction, Empirical Software Engineering, Springer, to appear. BibTeX
- OREAMUNO, E.L., KHAN, R.F., BANGASH, A.A., STINSON, C. and ADAMS, B. (2024). The State of Documentation Practices of Third-party Machine Learning Models and Datasets, IEEE Software, IEEE, to appear. BibTeX
- LIN, J., ADAMS, B. and HASSAN, A.E. (2024). On the Coordination of Vulnerability Fixes - An Empirical Study of Practices from 13 CVE Numbering Authorities , Empirical Software Engineering, Springer, to appear. BibTeX
- NAYEBI, M. and ADAMS, B. (2024). Image-based Communication on Social Coding Platforms, Journal of Software: Evolution and Process (JSEP), Wiley, to appear. BibTeX
Conference
- OLEWICKI, D., HABCHI, S. and ADAMS, B. (2024). An empirical study on code review activity prediction in practice, in Proceedings of the ACM on Software Engineering (PACMSE), Issue FSE 2024 (Porto de Galinhas, Brazil), to appear. (Acceptance ratio: 121/483=25.1%) BibTeX
- OLEWICKI, D., HABCHI, S., NAYROLLES, M., FARAMARZI, M., CHANDAR, S. and ADAMS, B. (2024). Towards Lifelong Learning for Software Analytics Models: Empirical Study on Brown Build and Risk Prediction, in Proceedings of the Software Engineering In Practice (SEIP) track at the 46th International Conference on Software Engineering, ICSE (Lisbon, Portugal), to appear. (Acceptance ratio: 45/120=37.5%) BibTeX
- MACEDO, M., TIAN, Y., COGO, F. and ADAMS, B. (2024). Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation, in Proceedings of the 2024 Special Event of AI Foundation Models and Software Engineering (FORGE), to appear. (Acceptance ratio: 8/16=50%) BibTeX (Best Paper Award)