FECAR: A Feature Selection Framework for Software Defect Prediction

Liu, Shulong; Chen, Xiang; Liu, Wangshu; Chen, Jiaqiang; Gu, Qing; Chen, Daoxu

doi:10.1109/compsac.2014.66

Cited by 57 publications

(47 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FST could identify and extract most useful features of the dataset for learning, and these features are very valuable for analysis and future prediction. In most cases, the classification accuracy using the reduced feature set equaled to or bettered than that of using the complete feature set [1,8,15,25,31]. Nevertheless, as stated by Hall [15] in some cases, FS degraded ML performance in cases where some features are eliminated which are highly predictive of very small areas of the instance space or some features which are not predictive are selected.…”

Section: Feature Selection Techniquesmentioning

confidence: 93%

“…Fundamentals to employ ML for effective and improved SFP are consideration of different software metrics [4] - [7], [11] - [14], [29,30], Feature Selection (FS) [1,8,9], [15] - [17], [25], [28,34] and Data Balancing (DB) [9,18], [35] - [38]. For SFP, many software metrics have been proposed but we favor to separate the studies according to the most frequently used metrics: Chidamber and Kemerer's (CK) Object Oriented Metrics (OOM) and McCabe and Halstead Static Code Metrics (SCM) [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Combined-Learning Based Framework for Improved Software Fault Prediction

Yohannese¹,

Li²

2017

IJCIS

View full text Add to dashboard Cite

Software Fault Prediction (SFP) is found to be vital to predict the fault-proneness of software modules, which allows software engineers to focus development activities on fault-prone modules, thereby prioritize and optimize tests, improve software quality and make better use of resources. In this regard, machine learning has been successfully applied to solve classification problems for SFP. Nevertheless, the presence of different software metrics, the redundant and irrelevant features and the imbalanced nature of software datasets have created more and more challenges for the classification problems. Therefore, the objective of this study is to independently examine software metrics with multiple Feature Selection (FS) combined with Data Balancing (DB) using Synthetic Minority Oversampling Techniques for improving classification performance. Accordingly, a new framework that efficiently handles those challenges in a combined form on both Object Oriented Metrics (OOM) and Static Code Metrics (SCM) datasets is proposed. The experimental results confirm that the prediction performance could be compromised without suitable Feature Selection Techniques (FST). To mitigate that, data must be balanced. Thus our combined technique assures the robust performance. Furthermore, a combination of Random Forts (RF) with Information Gain (IG) FS yields the highest Receiver Operating Characteristic (ROC) curve (0.993) value, which is found to be the best combination when SCM are used, whereas the combination of RF with Correlation-based Feature Selection (CFS) guarantees the highest ROC (0.909) value, which is found to be the best choice when OOM are used. Therefore, as shown in this study, software metrics used to predict the fault proneness of the software modules must be carefully examined and suitable FST for software metrics must be cautiously selected. Moreover, DB must be applied in order to obtain robust performance. In addition to that, dealing with the challenges mentioned above, the proposed framework ensures the remarkable classification performance and lays the pathway to quality assurance of software.

show abstract

Section: Feature Selection Techniquesmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A Combined-Learning Based Framework for Improved Software Fault Prediction

Yohannese¹,

Li²

2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…Chen et al [22] proposed a two-stage data preprocessing framework, TC, which combines feature selection and instance reduction. Liu et al [23] proposed a new feature selection framework, FECAR, to conduct feature clustering and feature ranking.…”

Section: B Feature Selection In Defect Predictionmentioning

confidence: 99%

“…We compare our method with six classical feature selection methods in defect prediction:(1)Full, (2)Chi-Square [28], (3) Signal-to-Noise [29], (4)Information Gain [30], (5)Gain Ratio [31], and (6)FSCAR [23].…”

Section: Research Questionsmentioning

confidence: 99%

See 1 more Smart Citation

FSCR:A Feature Selection Method for Software Defect Prediction

Ma²,

Ma³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Abstract-Prediction the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty. Some regression models have been used for predicting the number of faults. However, the software defect data may involve irrelevant and redundant module features, which will degrade the performance of these regression models. To address such issue, this paper proposes a feature selection method based on Feature Spectral Clustering and feature Ranking (FSCR) for the number of software faults prediction. First, FSCR groups the original features with spectral clustering according to the correlation between every two features. Second, FSCR employs ReliefF algorithm to compute the relevance between each feature with respect to the number of faults and selects top p most relevant features from each resulted cluster. We evaluate our proposed method on 6 widely-studied project datasets with four performance metrics. Comparison with five existing feature selection methods demonstrates that FSCR is effective in selecting features for the number of faults prediction.

show abstract

Multitask defect prediction

Chen

Xia

et al. 2019

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

Within‐project defect prediction assumes that we have sufficient labeled data from the same project, while cross‐project defect prediction assumes that we have plenty of labeled data from source projects. However, in practice, we might only have limited labeled data from both the source and target projects in some scenarios. In this paper, we want to apply multitask learning to investigate such a new scenario. To our best knowledge, this problem (ie, both the source project and the target project have limited labeled data) has not been thoroughly investigated, and we are the first to propose a novel multitask defect prediction approach mask. mask consists of a differential evolution optimization phase and a multitask learning phase. The former phase aims to find optimal weights for shared and nonshared information in related projects (ie, the target project and its related source projects), while the latter phase builds prediction models for each project simultaneously. To verify the effectiveness of mask, we perform experimental studies on 18 real‐world software projects and compare our approach with four state‐of‐the‐art baseline approaches: single‐task learning (STL), simple combined learning (SCL), Peters filter, and Burak filter. Experimental results show that mask can achieve F1 of 0.397 and AUC of 0.608 on average with a few labeled data (ie, 10% of data). Across the 18 projects, mask can outperform baseline methods significantly in terms of F1 and AUC. Therefore, by utilizing the relatedness among multiple projects, mask can perform significantly better than the state‐of‐the‐art methods. The results confirm that mask is promising for software defect prediction when the source and target projects both have limited training data.

show abstract

FECAR: A Feature Selection Framework for Software Defect Prediction

Cited by 57 publications

References 24 publications

A Combined-Learning Based Framework for Improved Software Fault Prediction

A Combined-Learning Based Framework for Improved Software Fault Prediction

FSCR:A Feature Selection Method for Software Defect Prediction

Multitask defect prediction

Contact Info

Product

Resources

About