Enhancing software defect prediction using supervised-learning based framework

Bashir, Kamal; Li, Tianrui; Yohannese, Chubato Wondaferaw

doi:10.1109/iske.2017.8258790

Cited by 21 publications

(12 citation statements)

References 21 publications

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“…The nature of the software prediction data such as presence of noise, unbalanced, and high dimensional data has an effect to a large extent on classifiying models . K. Bashir et al Proposed a joint framework to improve Software defect prediction models with the help of Iterative-Partition Filter to overcome the abnormality in the data [17]. In a data set, when the samples of one class is more than the other class, this type of problem is known as data imbalance problem.…”

Section: Literature Surveymentioning

confidence: 99%

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Kumar*,

Prasad

2020

IJITEE

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Software enhances the working capability of any business. Developing such a software entrusts the developing organization to build defect free software. In this context we have used PC1 dataset(NASA dataset) which has sufficient parameters for analysis. Intelligent techniques using different methodologies have been applied exhaustively on the PC1 data to find out the best intelligent technique for software defect. As the PC1 data is highly imbalanced data, there was biasness in the prediction of the intelligent techniques. Hence, to overcome this issue, in this paper we tried to propose best balancing method along with the intelligent technique to predict the software defect accurately.

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Section: Literature Surveymentioning

confidence: 99%

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Kumar*,

Prasad

2020

IJITEE

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“…Many studies apply different methods including FS to ensure the selection of most relevant feature subset for constructing prediction model with the view to enhance learning performance for classification and prediction for SDP. In a classical work on an empirical study of feature ranking techniques for software quality prediction by Khoshgoftaar et al [15], seven filter In a similar study to improve learning performance, Xu et al contended that the performance of models built for defect prediction is affected by high dimensionality in training data. To justify their claim, an extensive study was conducted [16] to investigate the impact of 32 FS methods on defect prediction over several datasets.…”

Section: Id:p0160mentioning

confidence: 99%

An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework

Bashir¹,

Li²,

Yohannese³

2019

IJCIS

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The object of software defect prediction (SDP) is to identify defect-prone modules. This is achieved through constructing prediction models using datasets obtained by mining software historical depositories. However, data mined from these depositories are often associated with high dimensionality, class imbalance, and mislabels which deteriorate classification performance and increase model complexity. In order to mitigate the consequences, this paper proposes an integrated preprocessing framework in which feature selection (FS), data balance (DB), and noise filtering (NF) techniques are fused to deal with the factors that deteriorate learning performance. We apply the proposed framework on three software metrics, namely static code metric (SCM), object oriented metric (OOM), and combined metric (CombM) and build models based on four scenarios (S): (S1) original data; (S2) FS subsets; (S3) FS subsets after DB using random under sampling (RUS) and synthetic minority oversampling technique (SMOTE); (S4) FS subsets after DB (RUS and SMOTE); and NF using iterative partitioning filter (IPF) and iterative noise filtering based on the fusing of classifiers (INFFC). Empirical results show that 1. the integrated preprocessing of FS, DB, and NF improves the performance of all the models built for SDP, 2. for all FS methods, all the models improve performance progressively from S2 through to S4 in all the software metrics, 3. model performance based on S4 is statistically significantly better than the performance based on S3 for all the software metrics, and 4. in order to achieve optimal model performance for SDP, appropriate implementation of the proposed framework is required. The results also validate the effectiveness of our proposal and provide guidelines for achieving quality training data that enhances model performance for SDP.

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“…To deal with these issues we have realized that the performance of ELA can be increased by keeping the quality of software defect datasets, which can be done by applying either FS, resolving class imbalance problem and/or filtering noise instances [12,13,18,25,26,41,42,44,47] from defect datasets. In this regard, Liu et al [45] made a comprehensive survey of FS algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, to deal with these issues, an ensemble based combined framework has to be designed specifically. Therefore, in this study, our plan is to combine ELA with Feature Selection (FS) [9, 12-14, 20, 21, 45-47], Data Balancing (DB) [11,20,[23][24][25]47] and Noise Filtering (NF) [36][37][38][39][40][41][42]44] techniques. FS is carried out by removing less important and redundant features, so that only beneficial features are left for training the models.…”

Section: Introductionmentioning

confidence: 99%

“…The other is to re-sample the original dataset, either by oversampling the minority class and/or under-sampling the majority class [11,23]. As shown in different studies [23,25,26,42], balancing data using Synthetic Minority Oversampling Techniques (SMOTE) gives better classification performance. Therefore, in Stage 2, this study explores the algorithm of SMOTE for building SFP models with datasets that suffer from class imbalance.…”

Section: Introductionmentioning

confidence: 99%

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A Three-Stage Based Ensemble Learning for Improved Software Fault Prediction: An Empirical Comparative Study

Yohannese¹,

Li²,

Bashir³

2018

IJCIS

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Software Fault Prediction (SFP) research has made enormous endeavor to accurately predict fault proneness of software modules, thus maximize precious software test resources, reduce maintenance cost and contributes to produce quality software products. In this regard, Machine Learning (ML) has been successfully applied to solve classification problems for SFP. However, SFP has many challenges that are created due to redundant and irrelevant features, class imbalance problem and the presence of noise in software defect datasets. Yet, neither of ML techniques alone handles those challenges and those may deteriorate the performance depending on the predictor's sensitiveness to data corruptions. In the literature, it is widely claimed that building ensemble classifiers from preprocessed datasets and combining their predictions is an interesting method of overcoming the individual problems produced by each classifier. This statement is usually not supported by thorough empirical studies considering problems in combined implementation with resolving different types of challenges in defect datasets and, therefore, it must be carefully studied. Thus, the objective of this paper is to conduct large scale comprehensive experiments to study the effect of resolving those challenges in SFP in three stages in order to improve the practice and performance of SFP. In addition to that, the paper presents a thorough and statistically sound comparison of these techniques in each stage. Accordingly, a new three-stage based ensemble learning framework that efficiently handles those challenges in a combined form is proposed. The experimental results confirm that the proposed framework has exhibited the robustness of combined techniques in each stage. Particularly high performance results have achieved using combined ELA on selected features of balanced data after removing noise instances. Therefore, as shown in this study, ensemble techniques used for SFP must be carefully examined and combined with techniques to resolve those challenges and obtain robust performance so as to accurately identify the fault prone software modules.

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Enhancing software defect prediction using supervised-learning based framework

Cited by 21 publications

References 21 publications

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework

A Three-Stage Based Ensemble Learning for Improved Software Fault Prediction: An Empirical Comparative Study

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