HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Alazzawi, Ammar K.; Rais, Helmi Md; Basri, Shuib; Alsariera, Yazan A.; Capretz, Luiz Fernando; Balogun, Abdullateef Oluwagbemiga; Imam, Abdullahi Abubakar

doi:10.15837/ijccc.2021.5.4308

Cited by 13 publications

(13 citation statements)

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“…A T-way generating approach for both a uniform and variable strength test suite called (ABCVS) is [33] by utilizing the ABC technique to reduce the overall size of a test suite while simultaneously improving the interaction between tests in the suite. Alazzawi et al [34] proposed a new meta-heuristic-based t-way approach called hybrid artificial bee colony (HABCSm). It combines the advantages of the ABC algorithm and PSO.…”

Section: Related Workmentioning

confidence: 99%

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Fadhil

Abdullah

Younis

2023

IJ-AI

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<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. This study presents a hybrid greedy hill climbing algorithm (HGHC) that ensures both effectiveness and near-optimal results for generating a small number of test data. To make certain that the suggested HGHC outperforms the most used techniques in terms of test size. It is compared to others in order to determine its effectiveness. In contrast to recent practices utilized for the production of covering arrays (CAs) and mixed covering arrays (MCAs), this hybrid strategy is superior since allowing it to provide the utmost outcome while reducing the size and limit the loss of unique pairings in the CA/MCA generation.</p>

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Section: Related Workmentioning

confidence: 99%

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Fadhil

Abdullah

Younis

2023

IJ-AI

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“…Bee Algorithm based t-way strategies are also implemented in sequence-based testing [28]. However, Hybrid Artificial Bee Colony (HABC) Algorithm has been effective for a hamming based t-way strategy targeting variable strength test set generation [29]. This strategy has performed very well compared to its counterparts.…”

Section: Related Workmentioning

confidence: 99%

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

2022

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“…The software development lifecycle (SDLC) is a formal framework that has been specifically planned and built for the production or development of high-quality software systems. To ensure a timely and reliable software system, gradual steps in the SDLC, such as requirement elicitation, software system review, software system design, and software system maintenance, must be closely followed and applied [ 1 – 3 ]. Nevertheless, since the SDLC step-by-step operations are done by professionals, human errors or failures are inevitable.…”

Section: Introductionmentioning

confidence: 99%

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Balogun

Basri

Mahamad

et al. 2021

Computational Intelligence and Neuroscience

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The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation-based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F-measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state-of-the-arts HFS methods.

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HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Cited by 13 publications

References 50 publications

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

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