Showing results 31 to 45 of 45
< previous
| Issue Date | Title | Author(s) |
| 2011 | New ideas and emerging research: evaluating prediction system accuracy | Shepperd, M |
| 20-Jul-2019 | A novel aggregation-based dominance for Pareto-based evolutionary algorithms to configure software product lines | Xue, Y; Li, M; Shepperd, M; Lauria, S; Liu, X |
| 27-May-2018 | Poster: Bridging effort-Aware prediction and strong classification: A just-in-Time software defect prediction study | Guo, Y; Shepperd, M; Li, N |
| 2011 | Predicting software project effort: A grey relational analysis based method | Song, Q; Shepperd, M |
| 2019 | The Prevalence of Errors in Machine Learning Experiments | Shepperd, M; Guo, Y; Li, N; Arzoky, M; Capiluppi, A; Counsell, S; Destefanis, G; Swift, S; Tucker, A; Yousefi, L |
| 2016 | Realistic assessment of software effort estimation models | Sigweni, B; Shepperd, M; Turchi, T |
| 15-Apr-2020 | Reasoning about uncertainty in empirical results | Walkinshaw, N; Shepperd, M |
| 3-Jun-2018 | Replication considered harmful | Shepperd, M |
| 2018 | Replication studies considered harmful | Shepperd, M |
| 2014 | Researcher bias: The use of machine learning in software defect prediction | Shepperd, M; Bowes, D; Hall, T |
| 31-Jan-2018 | The role and value of replication in empirical software engineering results | Shepperd, M; Ajienka, N; Counsell, S |
| 2012 | The scientific basis for prediction research | Shepperd, M |
| 22-Feb-2020 | A systematic review of unsupervised learning techniques for software defect prediction | Li, N; Shepperd, M; Guo, Y |
| 2015 | Using blind analysis for software engineering experiments | Sigweni, B; Shepperd, M |
| 17-Apr-2020 | Using the Lexicon from Source Code to Determine Application Domains | Capiluppi, A; Ajienka, N; Ali, N; Arzoky, M; Counsell, S; Destefanis, G; Miron, A; Nagaria, B; Neykova, R; Shepperd, M; Swift, S; Tucker, A |
