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Search results for biblio_year:2023
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2022
D. Binkley and L. Moonen. Assessing the Impact of Execution Environment on Observation-Based Slicing In IEEE Working Conference on Source Code Analysis and Manipulation. IEEE, 2022.PDF icon binkley_moonen_assessing_2022.pdf (212.12 KB)
D. Binkley and L. Moonen. Assessing the Impact of Execution Environment on Observation-Based Slicing. arXiv, 2022.PDF icon binkley2022_assessing_arxiv.pdf (237.97 KB)
D. Binkley, L. Moonen and S. Isaacman. Featherweight Assisted Vulnerability Discovery. arXiv, 2022.PDF icon binkley2022_featherweight_arxiv.pdf (709.01 KB)
D. Binkley, L. Moonen and S. Isaacman. "Featherweight Assisted Vulnerability Discovery." Information and Software Technology 146 (2022): 106844.PDF icon binkley2022_featherweight.pdf (782.28 KB)
D. Binkley, L. Moonen and S. Isaacman. Replication package for the paper Featherweight Assisted Vulnerability Discovery. Zenodo, 2022.
2020
L. Moonen, D. Binkley and S. Pugh. "On Adaptive Change Recommendation." Journal of Systems and Software 164 (2020).PDF icon moonen_-_on_adaptive_change_recommendation.pdf (1.44 MB)
2018
T. G. Rolfsnes, L. Moonen, S. Di Alesio, R. Behjati and D. Binkley. "Aggregating Association Rules to Improve Change Recommendation." Journal of Empirical Software Engineering (EMSE) 23, no. 2 (2018): 987-1035.PDF icon aggregating_association_rules_to_improve_change_recommendation.pdf (3.6 MB)
L. Moonen, T. G. Rolfsnes, D. Binkley and S. Di Alesio. Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact?. Zenodo, 2018.
L. Moonen, T. G. Rolfsnes, D. Binkley and S. Di Alesio. "What are the Effects of History Length and Age on Mining Software Change Impact?" Journal of Empirical Software Engineering (EMSE) 23, no. 4 (2018): 2362-2397.PDF icon 10.1007_s10664-017-9588-z.pdf (1.39 MB)
2017
T. G. Rolfsnes, L. Moonen and D. Binkley. Predicting Relevance of Change Recommendations In IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana-Champaign, Illinois, USA. IEEE, 2017.
T. G. Rolfsnes, L. Moonen and D. Binkley. Predicting Relevance of Change Recommendations In The IEEE/ACM International Conference on Automated Software Engineering (ASE), Edited by G. Ruso, M. Di Penta and T. N. Nguyen. IEEE, 2017.PDF icon predicting_relevance_of_change_recommendations.pdf (379.76 KB)
2016
T. G. Rolfsnes, L. Moonen, S. Di Alesio and D. Binkley. Exploring the Design Space of Association Rule Mining Algorithms for Change Recommendation. Simula Research Laboratory, 2016.
L. Moonen, S. Di Alesio, T. G. Rolfsnes and D. Binkley. Exploring the Effects of History Length and Age on Mining Software Change Impact. Simula Research Laboratory, 2016.
L. Moonen, S. Di Alesio, T. G. Rolfsnes and D. Binkley. Exploring the Effects of History Length and Age on Mining Software Change Impact In International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2016.PDF icon exploring_the_effects_of_history_length_and_age_on_mining_software_change_impact.pdf (468.7 KB)
T. G. Rolfsnes, S. Di Alesio, R. Behjati, L. Moonen and D. Binkley. Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis In 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER). IEEE, 2016.PDF icon tarmaq_saner2016.pdf (512.07 KB)
T. G. Rolfsnes, L. Moonen, S. Di Alesio, R. Behjati and D. Binkley. Improving Change Recommendation using Aggregated Association Rules In 13th International Conference on Mining Software Repositories (MSR). ACM, 2016.PDF icon improving_change_recommendation_using_aggregated_association_rules_-_msr_-_2016.pdf (251.68 KB)
L. Moonen, S. Di Alesio, D. Binkley and T. G. Rolfsnes. Practical Guidelines for Change Recommendation using Association Rule Mining. Simula Research Laboratory, 2016.PDF icon simula-tr-2016-09.pdf (437.07 KB)
L. Moonen, S. Di Alesio, D. Binkley and T. G. Rolfsnes. Practical Guidelines for Change Recommendation using Association Rule Mining In IEEE/ACM International Conference on Automated Software Engineering (ASE). ACM, 2016.PDF icon practical_guidelines_for_change_recommendation_using_association_rule_mining.pdf (634.33 KB)