Adapting a fault prediction model to allow inter languagereuse
Proceedings of the 4th international workshop on Predictor models in software engineering
Systematic literature reviews in software engineering - A tertiary study
Information and Software Technology
Refining the systematic literature review process--two participant-observer case studies
Empirical Software Engineering
State of the practice in software effort estimation: a survey and literature review
CEE-SET'08 Proceedings of the Third IFIP TC 2 Central and East European conference on Software engineering techniques
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Maximising data retention from the ISBSG repository
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
Systematic mapping studies in software engineering
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
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BACKGROUND: the availability of multi-organisation data sets has made it possible for individual organisations to build and apply management models, even if they do not have data of their own. In the absence of any data this may be a sensible option, driven by necessity. However, if both cross-company (or global) and within-company (or local) data are available, which should be used in preference? PROBLEM: several research papers have addressed this question but without any apparent convergence of results. METHOD: we conduct a systematic review of empirical studies comparing global and local effort prediction systems. RESULTS: we located 10 relevant studies: 3 supported global models, 2 were equivocal and 5 supported local models. CONCLUSION: the studies do not have converging results. A contributing factor is that they have utilised different local and global data sets and different experimental designs thus there is substantial heterogeneity. We identify the need for common response variables and for common experimental and reporting protocols.