Data accumulation and software effort prediction

MacDonell, Stephen G.; Shepperd, Martin

doi:10.1145/1852786.1852828

Cited by 18 publications

(18 citation statements)

References 14 publications

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“…However, it is not entirely clear if chronology was considered in all of them. Several later studies also involved comparisons with growing portfolio approaches (Lokan and Mendes 2009a;MacDonell and Shepperd 2010;Amasaki et al 2011;Lokan and Mendes 2012;Amasaki and Lokan 2015a, b).…”

Section: Chronological Splitting Approachesmentioning

confidence: 99%

“…A similar study in the context of data-based splitting demonstrates that date-based splitting can also sometimes lead to different results from random holdout (Lokan and Mendes 2009c). MacDonell and Shepperd (2010) also investigated growing portfolio in comparison with leave-one-out cross-validation based on least squares linear regression and a WC dataset. Their results suggest that these evaluation approaches lead to different results, even though their analysis is not based on statistical tests.…”

Section: Chronological Splitting Approachesmentioning

confidence: 99%

“…MacDonell and Shepperd (2010) investigated the predictive performance of fixedsize windows with size of five projects in a study based on least squares linear regression and the WC MacDonell dataset. Even though their analysis is not based on statistical tests, it suggests that moving windows can provide much better results than growing portfolio.…”

Section: Moving Window Approachesmentioning

confidence: 99%

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Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Minku

Yao

2016

Autom Softw Eng

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Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by A preliminary version of this work appeared as Minku and Yao (2012a

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Section: Chronological Splitting Approachesmentioning

confidence: 99%

Section: Chronological Splitting Approachesmentioning

confidence: 99%

Section: Moving Window Approachesmentioning

confidence: 99%

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Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Minku

Yao

2016

Autom Softw Eng

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“…In [59], MacDonell and Shepperd demonstrated empirically that failure to take the temporal nature of data accumulation into account leads to unreliable estimates of development effort. While much ESE research utilizes 'complete' data sets this represents an artificial scenario.…”

Section: )mentioning

confidence: 99%

A Taxonomy of Data Quality Challenges in Empirical Software Engineering

Bosu

MacDonell

2013

2013 22nd Australian Software Engineering Conference

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“…Similarly, where information on the ordering of observations is available this could be used to further inform the sampling strategy. Again, however, such an approach has not been used to any great extent in empirical software engineering research [6].…”

Section: Introductionmentioning

confidence: 99%