Hybridization of Cuckoo-ACO algorithm for test case prioritization

Panwar, Deepak; Tomar, Pradeep; Singh, Vijandra

doi:10.1080/09720510.2018.1466962

Cited by 27 publications

(11 citation statements)

References 5 publications

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“…[134], [46], [48]-[51] 2 Clustering [10], [12], [42], [43], [53], [57], [60], [61], [69], [71], [79], [85], [21], [89], [90], [93], [101]- [103], [111], [114], [117], [118], [25], [126], [129], [130], [132], [133], [27], [31]- [35] 35…”

Section: Discussionmentioning

confidence: 99%

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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Software quality can be assured by passing the process of software testing. However, software testing process involve many phases which lead to more resources and time consumption. To reduce these downsides, one of the approaches is to adopt test case prioritization (TCP) where numerous works has indicated that TCP do improve the overall software testing performance. TCP does have several kinds of techniques which have their own strengths and weaknesses. As for this review paper, the main objective of this paper is to examine deeper on machine learning (ML) techniques based on research questions created. The research method for this paper was designed in parallel with the research questions. Consequently, 110 primary studies were selected where, 58 were journal articles, 50 were conference papers and 2 considered as others articles. For overall result, it can be said that ML techniques in TCP has trending in recent years yet some improvements are certainly welcomed. There are multiple ML techniques available, in which each technique has specified potential values, advantages, and limitation. It is notable that ML techniques has been considerably discussed in TCP approach for software testing.

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Section: Discussionmentioning

confidence: 99%

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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“…Hybridization in ant colony optimization using the cuckoo search [20], and using GA [21,22] are also developed for effective feature selection process. Mafarja and Mirjalili developed a cohesive method to combine simulated annealing methods with the whale optimization approach for identifying the ideal set of features [23].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika

Selvamuthukumaran²

2021

Int J Comput Intell Syst

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Diabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

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“…To imitate this, the artificial in ACO ants performs a mobile search through positive feedback of volatility accumulation of pheromones to select the optimal path, which can avoid falling into the local optimum. Therefore, the key of the ACO lies in the movement rules and pheromone update [45][46][47][48]. The detailed steps of ACO-SVR are as follows:…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area

Zhang

Tang

Wen

et al. 2020

Sensors

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Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.

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Hybridization of Cuckoo-ACO algorithm for test case prioritization

Cited by 27 publications

References 5 publications

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area

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