Publications

Here is an overview of our publications:

  1. T. 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] [replication package]

  2. T. 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]

  3. L. Moonen, S. Di Alesio, D. Binkley, and T. Rolfsnes. Practical Guidelines for Change Recommendation using Association Rule Mining. In IEEE/ACM International Conference on Automated Software Engineering (ASE). ACM, 2016. [PDF] [replication package]

  4. L. Moonen, S. Di Alesio, T. Rolfsnes, and D. Binkley. Exploring the Effects of History Length and Age on Mining Software Change Impact. In IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2016 [PDF]

  5. T. Rolfsnes, L. Moonen, and D. Binkley. Predicting Relevance of Change Recommendations. In IEEE/ACM International Conference on Automated Software Engineering (ASE). ACM, 2017. [PDF] [replication package]

  6. T. Rolfsnes. Improving History-Based Change Recommendation Systems for Software Evolution. PhD thesis. Faculty of Mathematics and Natural Sciences, University of Oslo. 2017, No. 1885. ISSN 1501-7710. [PDF]

  7. T. Rolfsnes, L. Moonen, S. Di Alesio, R. Behjati and D. Binkley. Aggregating Association Rules to Improve Change Recommendation In Empirical Software Engineering (EMSE), 1–49, 2017, Springer. doi://10.1007/s10664-017-9560-y [PDF]

  8. L. Moonen, T. Rolfsnes, D. Binkley, and S. Di Alesio. What are the effects of history length and age on mining software change impact? In Empirical Software Engineering (EMSE), 1–36, 2018, Springer. doi://10.1007/s10664-017-9588-z [PDF] [replication package]