Ensembles and locality: Insight on improving software effort estimation

Minku, Leandro L.; Yao, Xin

doi:10.1016/j.infsof.2012.09.012

Cited by 132 publications

(174 citation statements)

References 38 publications

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“…As explained in Sect. 6.1, RTs have shown to produce good predictive performance for SEE in comparison to several other automated approaches (Minku and Yao 2013a), being a good baseline approach for this study. -RT trained both on the CC and WC data streams (CC-RT).…”

Section: Methodsmentioning

confidence: 95%

“…Regression trees (RTs) were used as the data models in the experiments, as they achieved good predictive performance for SEE in comparison to several other approaches (Minku and Yao 2013a). The input and output attributes of the RTs are the input and output attributes of the datasets explained in Sect.…”

Section: Methodsmentioning

confidence: 99%

“…This approach is also known as estimation by analogy. Despite being first used for SEE more than 15 years ago, this approach has been shown to be able to achieve competitive results in comparison to recent techniques, depending on the dataset (Minku and Yao 2013a).…”

Section: For See Assuming No Chronologymentioning

confidence: 99%

“…More recent work has been emphasising the relatively good predictive performance achieved by ensembles of learning machines (Kultur et al 2009;Minku and Yao 2013a;Kocaguneli et al 2012) and local methods that make estimations based on completed projects similar to the project being estimated (Minku and Yao 2013a;Menzies et al 2013;Bettenburg et al 2012). For instance, Regression Trees (RTs), Bagging ensembles of MultiLayer Perceptrons (Bag + MLPs) and Bagging ensembles of RTs (Bag + RTs) have been shown to perform well across several datasets (Minku and Yao 2013a).…”

Section: For See Assuming No Chronologymentioning

confidence: 99%

“…For instance, Regression Trees (RTs), Bagging ensembles of MultiLayer Perceptrons (Bag + MLPs) and Bagging ensembles of RTs (Bag + RTs) have been shown to perform well across several datasets (Minku and Yao 2013a).…”

Section: For See Assuming No Chronologymentioning

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: Methodsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Section: For See Assuming No Chronologymentioning

confidence: 99%

Section: For See Assuming No Chronologymentioning

confidence: 99%

Section: For See Assuming No Chronologymentioning

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

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Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Hosni

Idri

Abran

2018

J Software Evolu Process

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Researchers have developed and evaluated many techniques to deliver accurate estimates of the effort required to complete a new software program. Among these, analogy has emerged as a very promising technique, in particular the fuzzy analogy estimation technique that uses the fuzzy logic concepts in order to deal with both categorical and numerical data. The aim of this paper is twofold: (1) evaluate the impact of 3 filters on the predictive ability of single and ensemble fuzzy analogy techniques and (2) assess whether filters could be a source of diversity for fuzzy analogy ensembles. Moreover, it compares the filter single and ensemble fuzzy analogy techniques with fuzzy analogy ensembles built without using feature selection over 6 datasets. The overall results suggest that (1) more accurate estimates are generated when filters were used with single and ensemble fuzzy analogy techniques, (2) filter single fuzzy analogy techniques outperformed filter fuzzy analogy ensembles, and (3) fuzzy analogy ensembles without feature selection were more accurate than filter single and ensemble techniques. Therefore, though the use of feature selection techniques led single and ensemble fuzzy analogy to generate accurate estimations, they failed to be a source of diversity for fuzzy analogy ensembles. Hence, constructing fuzzy analogy homogenous ensembles that combine single fuzzy analogy techniques with different parameter configurations still generate better accuracy than filter fuzzy analogy ensembles. However, further empirical evaluations of filter/wrappers fuzzy analogy ensembles are required in order to confirm or refute these findings.

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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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Ensembles and locality: Insight on improving software effort estimation

Cited by 132 publications

References 38 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

Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

An extensive evaluation of ensemble techniques for software change prediction

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