Browsing by Author Shepperd, M

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Issue DateTitleAuthor(s)
2014Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluationSigweni, B; Shepperd, M
2010Framework to manage labels for e-assessment of diagramsJayal, Ambikesh
2011A general software defect-proneness prediction frameworkSong, Q; Jia, Z; Shepperd, M; Ying, S; Liu, J
2011Group project work from the outset: an in-depth teaching experience reportShepperd, M
2015How do i know whether to trust a research result?Shepperd, M
2014The impact of communication on trust in agile methodsHasnian, Eisha
2018Inferencing into the void: problems with implicit populations Comments on `Empirical software engineering experts on the use of students and professionals in experiments'Shepperd, M
2009Integrate the GM(1,1) and Verhulst models to predict software stage effortWang, Y; Song, Q; MacDonell, S; Shepperd, M; Shen, J
2017The interlocutory tool box: techniques for curtailing coincidental correctnessPatel, Krishna
2016An investigation of feature weighting algorithms and validation techniques using blind analysis for analogy-based estimationSigweni, Boyce B.
2011New ideas and emerging research: evaluating prediction system accuracyShepperd, M
20-Jul-2019A novel aggregation-based dominance for Pareto-based evolutionary algorithms to configure software product linesXue, Y; Li, M; Shepperd, M; Lauria, S; Liu, X
27-May-2018Poster: Bridging effort-Aware prediction and strong classification: A just-in-Time software defect prediction studyGuo, Y; Shepperd, M; Li, N
2011Predicting software project effort: A grey relational analysis based methodSong, Q; Shepperd, M
2019The Prevalence of Errors in Machine Learning ExperimentsShepperd, M; Guo, Y; Li, N; Arzoky, M; Capiluppi, A; Counsell, S; Destefanis, G; Swift, S; Tucker, A; Yousefi, L
2016Realistic assessment of software effort estimation modelsSigweni, B; Shepperd, M; Turchi, T
15-Apr-2020Reasoning about uncertainty in empirical resultsWalkinshaw, N; Shepperd, M
3-Jun-2018Replication considered harmfulShepperd, M
2018Replication studies considered harmfulShepperd, M
2014Researcher bias: The use of machine learning in software defect predictionShepperd, M; Bowes, D; Hall, T