Showing results 25 to 44 of 48
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Issue Date | Title | Author(s) |
2015 | How do i know whether to trust a research result? | Shepperd, M |
2014 | The impact of communication on trust in agile methods | Hasnian, Eisha |
17-Jun-2021 | The impact of using biased performance metrics on software defect prediction research | Yao, J; Shepperd, M |
18-Jun-2024 | Improving classifier-based effort-aware software defect prediction by reducing ranking errors | Guo, Y; Shepperd, M; Li, N |
2018 | Inferencing into the void: problems with implicit populations Comments on `Empirical software engineering experts on the use of students and professionals in experiments' | Shepperd, M |
2009 | Integrate the GM(1,1) and Verhulst models to predict software stage effort | Wang, Y; Song, Q; MacDonell, S; Shepperd, M; Shen, J |
2017 | The interlocutory tool box: techniques for curtailing coincidental correctness | Patel, Krishna |
2016 | An investigation of feature weighting algorithms and validation techniques using blind analysis for analogy-based estimation | Sigweni, Boyce B. |
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 |
21-May-2022 | Problem reports and team maturity in agile automotive software development | Gren, L; Shepperd, M |
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 |