Software defect prediction: A multi-criteria decision-making approach

Balogun, Abdullateef Oluwagbemiga; Bajeh, Amos Orenyi; Mojeed, Hammed A.; Akintola, Abimbola G.

doi:10.4314/njtr.v15i1.7

Cited by 11 publications

(6 citation statements)

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“…Software defect datasets can be seen as errors or mistakes made in the past during the development of software systems. These errors are leveraged upon to predict the future occurrence of defect or the number of defects [40,41]. More specifically, software defect datasets can show specific components or software modules that are defect prone.…”

Section: Software Defect Datasetsmentioning

confidence: 99%

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

et al. 2020

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Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC values. Scott–KnottESD and the novel Double Scott–KnottESD rank statistical methods were used for statistical ranking of the studied FS methods. The experimental results showed that there is no one best FS method as their respective performances depends on the choice of classifiers, performance evaluation metrics, and dataset. However, we recommend the use of statistical-based, probability-based, and classifier-based filter feature ranking (FFR) methods, respectively, in SDP. For filter subset selection (FSS) methods, correlation-based feature selection (CFS) with metaheuristic search methods is recommended. For wrapper feature selection (WFS) methods, the IWSS-based WFS method is recommended as it outperforms the conventional SFS and LHS-based WFS methods.

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Section: Software Defect Datasetsmentioning

confidence: 99%

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

et al. 2020

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“…In [ 18 ], an extensive and in-depth review of MCS-based methods was carried out in which quite a large number of ensemble learning techniques were closely and carefully examined in terms of their synthesis, variety, and dynamic updates. So far, by applying these systems, a large number of practical issues have been addressed [ 8 ], such as problems in face recognition [ 37 ], anomaly detection [ 21 , 22 ], credit scoring [ 3 ], speech recognition [ 45 , 47 ], recommender system [ 35 , 36 ], software bug prediction [ 1 , 29 ], intrusion detection [ 2 , 25 , 32 ] and remote sensing [ 14 , 27 , 31 ] as well as having been successfully used to tackle problems on changing environments [ 24 ]. In very recent years, new applications of MCSs have been explored regarding imbalanced data problems [ 6 , 16 , 19 , 43 ] and related biological datasets to handle disease detection problems such as COVID-19 diagnosis [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A novel framework based on the multi-label classification for dynamic selection of classifiers

Elmi

Eftekhari

Mehrpooya

et al. 2023

Int. J. Mach. Learn. & Cyber.

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Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have been introduced and applied to MCSs. Dealing with each test sample classification, DS methods seek to perform the task of classifier selection so that only the most competent classifiers are selected. The principal subject regarding DS techniques is how the competence of classifiers corresponding to every new test sample classification task can be estimated. In traditional dynamic selection methods, for classifying an unknown test sample x , first, a local region of data that is similar to x is detected. Then, those classifiers that efficiently classify the data in the local region are also selected so as to perform the classification task for x . Therefore, the main effort of these methods is focused on one of the two following tasks: (i) to provide a measure for identifying a local region, or (ii) to provide a criterion for measuring the efficiency of classifiers in the local region (competence measure). This paper proposes a new version of dynamic selection techniques that does not follow the aforementioned approach. Our proposed method uses a multi-label classifier in the training phase to determine the appropriate set of classifiers directly (without applying any criterion such as a competence measure). In the generalization phase, the suggested method is employed efficiently so as to predict the appropriate set of classifiers for classifying the test sample x . It is remarkable that the suggested multi-label-based framework is the first method that uses multi-label classification concepts for dynamic classifier selection. Unlike the existing meta-learning methods for dynamic ensemble selection in the literature, our proposed method is very simple to implement and does not need meta-features. As the experimental results indicate, the suggested technique produces a good performance in terms of both classification accuracy and simplicity which is fairly comparable with that of the benchmark DS techniques. The results of conducting the Quade non-parametric statistical test corroborate the clear dominance of the proposed method over the other benchmark methods.

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“…In other words, having defects in software systems will lead to degraded and unreliable software systems. In addition, software failures can generate dissatisfaction from end-users and stakeholders alike as failed software does not meet user requirement(s) after resources (time and effort) have been expiated [4,5]. Hence, it is imperative to consider early prediction and detection of software defects before software release.…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

et al. 2021

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Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture of filter methods and evaluate the results. This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). The proposed methods combine rank lists generated by individual filter methods using rank aggregation mechanisms into a single aggregated rank list. The proposed methods aim to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a dis-joint and complete feature rank list superior to individual filter rank methods. The effectiveness of the proposed method was evaluated with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed methods had a superior impact (positive) on prediction performances of NB and DT models than other experimented FS methods. This makes the combination of filter rank methods a viable solution to filter rank selection problem and enhancement of prediction models in SDP.

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Software defect prediction: A multi-criteria decision-making approach

Cited by 11 publications

References 0 publications

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

A novel framework based on the multi-label classification for dynamic selection of classifiers

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

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