CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines

Ferrer, Javier; Chicano, Francisco; Ortega-Toro, José Antonio

doi:10.1007/s10732-020-09462-w

Cited by 17 publications

(4 citation statements)

References 16 publications

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“…From the experiment results, it is demonstrated that CMSA can achieve similar performance compared to the exact solver for small to medium sized instances, while its performance is significantly better than the exact solver when it came to large instances. See [3,19,5,18] for recent research using CMSA.…”

Section: Hybrid Heuristicsmentioning

confidence: 99%

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

Copado-Méndez

Lin

Barrena

et al. 2022

Communications in Computer and Information Science

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This paper presents a K-Prototype assisted hybrid heuristic approach called SLIM+KP for solving large instances of the Train Unit Scheduling Optimization (TUSO) problem. TUSO is modelled as an Integer Multi-commodity Flow Problem (IMCFP) based on a Directed Acyclic Graph (DAG). When the problem size goes large, the exact solver is unable to solve it in reasonable time. Our method uses hybrid heuristics by iteratively solving reduced instances of the original problem where only a subset of the arcs in the DAG are heuristically chosen to be optimised by the same exact solver. K-Prototype is a clustering method for partitioning. It is an improvement of K-Means and K-Modes to handle clustering with the mixed data types. Our approach is designed such that the arcs of the DAG are clustered by K-prototype and each time only a small fraction of the arcs are selected to form the reduced instances. The capabilities of this framework were tested by realworld cases from UK train operating companies and compared with the results from running an exact integer solver. Preliminary results indicate the the proposed methodology achieves the same optimal solutions as the exact solver for small instances but within shorter time, and yields good solutions for instances that were intractable for the exact solver.

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Section: Hybrid Heuristicsmentioning

confidence: 99%

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

Copado-Méndez

Lin

Barrena

et al. 2022

Communications in Computer and Information Science

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“…is goaldriven model is different from the traditional object-oriented method but adopts the goal-oriented method, combines the software goal with the whole development process and product quality, and measures it step by step. is topdown approach to goal quality definition makes the measurement of the entire software comprehensive and straightforward [6]. Strandberg et al proposed a method of abnormal feature mining and feature detection for portable multidimensional control software testing based on particle swarm evolutionary optimization control.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Qiu

2022

Security and Communication Networks

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In order to improve the anomaly detection ability of portable multidimensional control software test data, a software test data anomaly detection method based on K-means clustering is proposed. The abnormal data distribution structure model of portable multidimensional control software testing is constructed. The fuzzy semantic feature reconstruction method is adopted to identify the fuzzy parameters of portable multidimensional control software and extract the feature quantity of associated information. According to the evolution distribution of associated features, the joint combination feature analysis method is adopted to realize the fuzzy clustering center detection of abnormal data of portable multidimensional control software test, and the fusion of abnormal feature distribution is carried out to complete the joint multidimensional feature detection. The K-means clustering method is used for effective data combination control in portable multidimensional control software to extract and detect abnormal features of test data in portable multidimensional control software. Experimental results show that the accuracy of anomaly detection of software test data by the proposed method is always greater than 0.8. Conclusion. Using this method to detect abnormal data of portable multidimensional control software test has higher accuracy and better detection performance.

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“…To alleviate this obstacle, pairwise testing has been widely used as a viable solution. The ultimate goal of pairwise testing is to cover all possible pair of features at least once [8]- [10]. Thus, testing can be focused on the interaction of both features.…”

Section: Pairwise Testingmentioning

confidence: 99%

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedybased and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

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CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines

Cited by 17 publications

References 16 publications

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

A K-Prototype Clustering Assisted Hybrid Heuristic Approach for Train Unit Scheduling

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

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