Feature selection for software effort estimation with localized neighborhood mutual information

Qin, Liu; Xiao, Jiakai; Zhu, Hongming

doi:10.1007/s10586-018-1884-x

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…They conducted experiments on six datasets and the results showed that CFS ensembles achieved better performance than RReliefF ensembles. Liu et al [30] proposed a greedy feature selection method, called LFS, to guarantee the appropriateness of case based reasoning for software effort estimation task. They conducted experiments on six datasets and the results showed that the feature subset selected by LFS made effective estimation compared with a randomized baseline method.…”

Section: B Feature Selection In Software Engineeringmentioning

confidence: 99%

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Zhao

Liu

Zhou

et al. 2021

Preprint

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The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.

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Section: B Feature Selection In Software Engineeringmentioning

confidence: 99%

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Zhao

Liu

Zhou

et al. 2021

Preprint

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“…Therefore, performing simultaneous feature selection (FS) and parameter optimization (PO) will enhance the accuracy of SDCPM. Principally, the FS preprocessing step will make the data purer by removing unimportant features [5], while the PO step will find the best configuration that enhances the performance of the used SDCPM. Generally, FS methods are grouped into three categories: the embedded, filter, and wrapper technique.…”

Section: Introductionmentioning

confidence: 99%

Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

Najm

Zakrani²,

Marzak

2021

MATEC Web Conf.

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The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.

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“…Depending on dataset used, the preprocessing data can be cleaning data by imputing missing value or transforming and/or reducing the data by removing redundant and irrelevant features. As one of the major concerns when using dataset to construct a SDEE model is the negative impact of irrelevant and redundant information on estimation accuracy [8].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, we need to remove irrelevant and redundant information and keep a subset of relevant features so only information about the effort (dependent variable) is reserved. For this purpose, many feature selection (FS) methods have been employed in the literature [8][9][10][11][12][13]. In this context, this paper aims to investigate the use of two feature selection methods as preprocessing step before feeding data to SVR model building stage.…”

Section: Introductionmentioning

confidence: 99%

Improving Software Development effort estimating using Support Vector Regression and Feature Selection

Zakrani¹,

Hain²,

Idri

2019

IJ-AI

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Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

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Feature selection for software effort estimation with localized neighborhood mutual information

Cited by 19 publications

References 15 publications

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

Improving Software Development effort estimating using Support Vector Regression and Feature Selection

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