Bagging Predictors for Estimation of Software Project Effort

Braga, Petrônio L.; Oliveira, Adriano L. I.; Ribeiro, G.H.T.; Meira, Sílvio R. L.

doi:10.1109/ijcnn.2007.4371196

Cited by 64 publications

(57 citation statements)

References 11 publications

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“…This analysis also complements RQ3: if a company has no resources to perform experiments for choosing a model, RTs are more likely to perform comparatively well and can be used for being comprehensive and having faster training. It is worth to note that, even though this work provides a different (practically the opposite) conclusion from Braga et al [6], it does not necessarily contradict their reported results. Considering that the best performances obtained by their ensembles and single learners is very similar in their experiments, had statistical tests been done, their conclusion could possibly have been more similar to ours.…”

Section: Step 2: Approaches Usually Among the Bestcontrasting

confidence: 74%

“…More important than checking what approaches are usually among the best is to gain insight on how to improve SEE further based on an analysis of these approaches. None of the previous papers involving ensembles [6,21,19] provide an analysis of the reasons for the obtained results. Differently from the literature, this section provides insight on how to improve SEE using ML techniques based on experimental studies, not just an intuition or speculation, being a key contribution of this paper.…”

Section: Approaches Singled Outmentioning

confidence: 97%

“…We recommend Jorgensen and Shepperd's work [17] for a systematic review. Among the proposed methods, effort estimators based on machine learning (ML) approaches such as multilayer perceptrons (MLPs), radial basis function (RBF) networks and regression trees (RTs) [29,33,15,2,30,21,6] have been receiving increased attention [17]. The motivation behind the use of such approaches is that they make no or minimal assumptions about the function being modelled and the data used for training.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Braga et al [6], Kultur et al [21] and Kocaguneli et al [19] investigated the use ensembles of learning machines for SEE. Ensembles of learning machines are sets of learners 1 trained to perform the same task and combined with the aim of improving predictive performance [9].…”

Section: Introductionmentioning

confidence: 99%

“…The study presented by Braga et al [6] claims that Bootstrap Aggregating (Bagging) [7], a well known ensemble learning approach, improves the SEEs produced by several single learner methods, such as RTs and MLPs. However, the study uses only two versions of a single data set and neither tests the statistical significance of the results nor presents the standard deviations of the performance.…”

Section: Introductionmentioning

confidence: 99%

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A principled evaluation of ensembles of learning machines for software effort estimation

Minku

Yao

2011

Proceedings of the 7th International Conference on Predictive Models in Software Engineering

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Background: Software effort estimation (SEE) is a task of strategic importance in software management. Recently, some studies have attempted to use ensembles of learning machines for this task. Aims: We aim at (1) evaluating whether readily available ensemble methods generally improve SEE given by single learning machines and which of them would be more useful; getting insight on (2) how to improve SEE; and (3) how to choose machine learning (ML) models for SEE. Method: A principled and comprehensive statistical comparison of three ensemble methods and three single learners was carried out using thirteen data sets. Feature selection and ensemble diversity analyses were performed to gain insight on how to improve SEE based on the approaches singled out. In addition, a risk analysis was performed to investigate the robustness to outliers. Therefore, the better understanding/insight provided by the paper is based on principled experiments, not just an intuition or speculation. Results: None of the compared methods is consistently the best, even though regression trees and bagging using multilayer perceptrons (MLPs) are more frequently among the best. These two approaches usually perform similarly. Regression trees place more important features in higher levels of the trees, suggesting that feature weights are important when using ML models for SEE. The analysis of bagging with MLPs suggests that a self-tuning ensemble diversity method may help improving SEE. Conclusions: Ideally, principled experiments should be done in an individual basis to choose a model. If an organisation has no resources for that, regression trees seem to be a good choice for its simplicity. The analysis also suggests approaches to improve SEE.

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Section: Step 2: Approaches Usually Among the Bestcontrasting

confidence: 74%

Section: Approaches Singled Outmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A principled evaluation of ensembles of learning machines for software effort estimation

Minku

Yao

2011

Proceedings of the 7th International Conference on Predictive Models in Software Engineering

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An extensive evaluation of ensemble techniques for software change prediction

Catolino

Ferrucci

2019

J Software Evolu Process

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Predicting the areas of the source code having a higher likelihood to change in the future represents an important activity to allow developers to plan preventive maintenance operations.For this reason, several change prediction models have been proposed. Moreover, research community demonstrated how different classifiers impact on the performance of devised models as well as classifiers tend to perform similarly even though they are able to correctly predict the change proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we deeper investigated whether the use of ensemble approaches, ie, machine learning techniques able to combine multiple classifiers, can improve the performances of change prediction models. Specifically, we built three change prediction models based on different predictors, ie, product-, process-metrics-, and developer-related factors, comparing the performances of four ensemble techniques (ie, Boosting, Random Forest, Bagging, and Voting) with those of standard machine learning classifiers (ie, Logistic Regression, Naive Bayes, Simple Logistic, and Multilayer Perceptron). The study was conducted on 33releases of 10 open-source systems, and the results showed how ensemble methods and in particular Random Forest provide a significant improvement of more than 10% in terms of F measure. Indeed, the statistical analyses conducted confirm the superiority of this ensemble technique. Moreover, the model built using developer-related factors performed better than the other models that exploit product and process metrics and achieves an overall median of F measure around 77%.

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On the value of filter feature selection techniques in homogeneous ensembles effort estimation

Hosni

Idri

Abran

2021

J Software Evolu Process

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Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the features that feed the ML technique, selecting the appropriate features in the preprocessing data step is important. This paper investigates three filter feature selection techniques to check the predictive capability of four single ML techniques: K-nearest neighbor, support vector regression, multilayer perceptron, and decision trees and their homogeneous ensembles over six well-known datasets.Furthermore, the single and ensembles techniques were optimized using the grid search optimization method. The results suggest that the three filter feature selection techniques investigated improve the reasonability and the accuracy performance of the four single techniques. Moreover, the homogeneous ensembles are statistically more accurate than the single techniques. Finally, adopting a random process (i.e., random subspace method) to select the inputs feature for ML technique is not always effective to generate an accurate homogeneous ensemble.

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Bagging Predictors for Estimation of Software Project Effort

Cited by 64 publications

References 11 publications

A principled evaluation of ensembles of learning machines for software effort estimation

A principled evaluation of ensembles of learning machines for software effort estimation

An extensive evaluation of ensemble techniques for software change prediction

On the value of filter feature selection techniques in homogeneous ensembles effort estimation

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