Data Mining Techniques for Software Effort Estimation: A Comparative Study

Dejaeger, Karel; Verbeke, Wouter; Martens, David; Baesens, Bart

doi:10.1109/tse.2011.55

Cited by 200 publications

(198 citation statements)

References 85 publications

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“…For analysing multiple repeated measures (for example, studies where many different cost estimation algorithms are applied to many different data sets), software engineering researchers have often adopted Friedman's test with corresponding post-hoc tests as recommended by Demšar (2006) (see, for example, Dejaeger et al 2012). However, in a study of the performance of Friedman's test, Agresti and Pendergast (1986) found that for an underlying normal distribution, their rank transformed ANOVA test could be substantially more powerful than the Friedman test.…”

Section: Non-parametric Testsmentioning

confidence: 99%

Robust Statistical Methods for Empirical Software Engineering

et al. 2016

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There have been many changes in statistical theory in the past 30 years, including increased evidence that non-robust methods may fail to detect important results. The statistical advice available to software engineering researchers needs to be updated to address these issues. This paper aims both to explain the new results in the area of robust analysis methods and to provide a large-scale worked example of the new methods. We summarise the results of analyses of the Type 1 error efficiency and power of standard parametric and non-parametric statistical tests when applied to non-normal data sets. We identify parametric and non-parametric methods that are robust to non-normality. We present an analysis of a large-scale software engineering experiment to illustrate their use. We illustrate the use of kernel density plots, and parametric and non-parametric methods using four different software engineering data sets. We explain why the methods are necessary and the rationale for selecting a specific analysis. We suggest using kernel density plots rather than box plots to visualise data distributions. For parametric analysis, we recommend trimmed means, which can support reliable tests of the differences between the central location of two or

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Section: Non-parametric Testsmentioning

confidence: 99%

Robust Statistical Methods for Empirical Software Engineering

et al. 2016

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“…Based on expert judgment [11] Based on machine learning methods [12] Linear and non-linear [7] Algorithmic Multiple model [10] Discrete models [8] Function models [9] equivalent or better accuracy than estimation with all features. In SDEE, both the filter [34] and wrapper [34] FS techniques have been used.…”

Section: Non-algorithmicmentioning

confidence: 99%

“…In the ML techniques, patterns of the old project data are learned, and can be used for effort prediction in new projects [1]. In SDEE, many researches have been performed by the ML approach [12]. In ML, a supervised method learns a model from the labeled training data.…”

Section: Introductionmentioning

confidence: 99%

“…Where the costs or efforts are calculated as numeric values in software projects, regression methods are studied as the ML models in SDEE researches. The ML algorithms in SDEE are divided into 6 categories [3] as case base reasoning (CBR) [12], artificial neural networks (ANNs) [14], decision trees (DT) [15], Bayesian networks (BN) [16], support vector regression (SVR) [14], and association rules (AR) [17]. Searching for useful and effective subset of features is known as the approach in the ML area to increase the learning model accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have focused on the efficiency improvement in SDEE by reducing the sample project features [34]. Different ML algorithms have been compared in [12] to estimate the cost of the software with different datasets. The effect of backward selection on each ML algorithm was studied in this work.…”

Section: Introductionmentioning

confidence: 99%

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Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

Beiranvand

Chahooki

2016

JAIDM

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Software project management is one of the significant activates in the software development process. Software development effort estimation (SDEE) is a challenging task in the software project management. SDEE has been an old activity in computer industry from 1940s, and thus it has been reviewed for several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before a software project contract. Due to the uncertain nature of development estimates, and in order to increase the accuracy, researchers have recently focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this work, for narrowing the semantic gap in SDEE, a hierarchical filter and wrapper feature selection (FS) techniques and fused measurement criteria are developed in a two-phase approach. In the first phase, the two-stage filter FS methods provide start sets for the wrapper FS techniques. In the second phase, a fused criterion is proposed to evaluate the accuracy in wrapper FS techniques. The experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.

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