Applying moving windows to software effort estimation

Lokan, Chris; Mendes, Emilia

doi:10.1109/esem.2009.5316019

Cited by 53 publications

(88 citation statements)

References 9 publications

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“…This approach was proposed due to the observation that the best fitting regression model changed substantially over time in their case study. Lokan and Mendes (2009a)'s work is to the best of our knowledge the first work to provide a detailed investigation of whether moving windows can improve predictive performance. Their work revealed that SEE models trained on fixed-size windows can provide significantly better predictive performance than the growing portfolio approach (Lokan and Mendes 2009a).…”

Section: Moving Window Approachesmentioning

confidence: 99%

“…Lokan and Mendes (2009a)'s work is to the best of our knowledge the first work to provide a detailed investigation of whether moving windows can improve predictive performance. Their work revealed that SEE models trained on fixed-size windows can provide significantly better predictive performance than the growing portfolio approach (Lokan and Mendes 2009a). However, whether or not fixed-size windows were beneficial depended on the window size.…”

Section: Moving Window Approachesmentioning

confidence: 99%

“…Amasaki and Lokan (2015a) also compared fixed-size windows against growing portfolio as part of their study with the same WC ISBSG dataset as (Lokan and Mendes 2009a). However, they used linear regression based on input attributes selected with Lasso (Tibshirani 1996) instead of stepwise regression.…”

Section: Moving Window Approachesmentioning

confidence: 99%

“…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%

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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: Moving Window Approachesmentioning

confidence: 99%

Section: Moving Window Approachesmentioning

confidence: 99%

Section: Moving Window Approachesmentioning

confidence: 99%

Section: Chronological Splitting 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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“…According to Lokan and Mendes [5] [12], selection of training and validation sets from a subset of chronologically arranged projects (referred to as moving window) can further improve the prediction accuracy of effort estimation models. The moving window theory is based on the assumption that recent projects are likely to share similar characteristics with new projects.…”

Section: Introductionmentioning

confidence: 99%

A Stratification and Sampling Model for Bellwether Moving Window

Mensah

Keung

Bosu³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-An effective method for finding the relevant number (window size) and the elapsed time (window age) of recently completed projects has proven elusive in software effort estimation. Although these two parameters significantly affect the prediction accuracy, there is no effective method to stratify and sample chronological projects to improve prediction performance of software effort estimation models. Exemplary projects (Bellwether) representing the training set have been empirically validated to improve the prediction accuracy in the domain of software defect prediction. However, the concept of Bellwether and its effect have not been empirically proven in software effort estimation as a method of selecting exemplary/relevant projects with defined window size and age. In view of this, we introduce a novel method for selecting relevant and recently completed projects referred to as Bellwether moving window for improving the software effort prediction accuracy. We first sort and cluster a pool of N projects and apply statistical stratification based on Markov chain modeling to select the Bellwether moving window. We evaluate the proposed approach using the baseline Automatically Transformed Linear Model on the ISBSG dataset. Results show that (1) Bellwether effect exist in software effort estimation dataset, (2) the Bellwether moving window with a window size of 82 to 84 projects and window age of 1.5 to 2 years resulted in an improved prediction accuracy than the traditional approach.

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On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation

Amasaki

Lokan

2014

J Software Evolu Process

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In construction of an effort estimation model, it seems effective to use a window of training data so that the model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. In this study, we examined the effects of weighted moving windows on effort estimation accuracy. We compared weighted and non-weighted moving windows under the same experimental settings. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, although the methods also significantly worsened accuracy in smaller windows. This result contributes to understanding properties of moving windows.

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Applying moving windows to software effort estimation

Cited by 53 publications

References 9 publications

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

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

A Stratification and Sampling Model for Bellwether Moving Window

On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation

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