An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

Khurma, Ruba Abu; Alsawalqah, Hamad; Aljarah, Ibrahim; Elaziz, Mohamed Abd; Damaševičius, Robertas

doi:10.3390/math9151722

Cited by 36 publications

(30 citation statements)

References 49 publications

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“…In particular, Haouari, Souici-Meslati [43] investigated the performance of artificial immune systems, which are bio-inspired ML techniques based on the mammalian immune paradigms for SDP. Also, Khurma, Alsawalqah [44] proposed an efficient binary variant of moth flame optimization (BMFO) for SDP. Concerning unsupervised ML techniques, Xu, Li [45] investigated the applicability and performance of 40 clustering techniques for SDP.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Balogun

Basri

Capretz

et al. 2021

Entropy

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Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter–filter relationships in selecting optimal features for SDP processes.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Balogun

Basri

Capretz

et al. 2021

Entropy

View full text Add to dashboard Cite

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“…In the software bug detection also, feature selection can be utilized to efficient software bug detection. This selection and weighting parameter selection is achieved with the assistance of the optimization algorithm such as Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) [14,15]. This selected the optimization algorithm is affected by the convergence analysis.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Deep Learning Model for Software Bug Detection and Classification

Sivapurnima¹,

Manjula²

2023

Computer Systems Science and Engineering

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Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with classification. Initially, the bug report input dataset is gathered from the online source system. In the pre-processing phase, the unwanted information from the input data are removed by using cleaning text, convert data types and null value replacement. The pre-processed data is sent into the feature extraction phase. In the feature extraction phase, the four types of feature extraction method are utilized such as contextual, categorical, temporal and textual. Finally, the features are sent to the proposed ADLM for automatic duplication bug report detection and classification. The proposed methodology is proceeding with two phases such as training and testing phases. Based on the working process, the bugs are detected and classified from the input data. The projected technique is assessed by analyzing performance metrics such as accuracy, precision, Recall, F_Measure and kappa.

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“…Since feature selection is an NP-complete problem [33], SI algorithms are widely used to solve this problem. Many researchers have adapted different SI algorithms for converting from the continuous form to binary, such as wrapper-based binary sine cosine algorithm (WBSCA) [34], binary grasshopper optimization algorithm (BGOA) [35], binary butterfly algorithm (BBA) [36], efficient binary symbiotic organisms search (EBSOS) [37], binary grey wolf optimizer with support vector machine (GWOSVM) [38], and island binary moth-flame optimization (IsBMFO) [39]. However, most binary SI algorithms are not effective or scalable enough to select effective features from large datasets as well as small ones.…”

Section: Introductionmentioning

confidence: 99%

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

et al. 2021

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Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

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An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

Cited by 36 publications

References 49 publications

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Adaptive Deep Learning Model for Software Bug Detection and Classification

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

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