The Genetic Algorithm and Binary Search Technique in the Program Path Coverage for Improving Software Testing Using Big Data

Alhroob, Aysh; Alzyadat, Wael; Imam, Ayad Tareq; Jaradat, Ghaith M.

doi:10.32604/iasc.2020.010106

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…French economist Pareto [17] first studied the multi-objective optimisation problem in economics and developed the Pareto optimality Theory. The multi-objective optimisation problem needs to optimise a set of functions whose solution is a set of points [18][19][20], called the Pareto optimal set. The Pareto optimal solution definition is as follows:…”

Section: Multi Objective Optimisationmentioning

confidence: 99%

Model of a Composite Energy Storage System for Urban Rail Trains

Liang¹,

Qi²,

Liang³

2022

Computer Systems Science and Engineering

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Urban rail transit can solve the current inconvenient transportation problem for China's large urban population. A compound onboard energy storage system can meet vehicles' traction requirements and recover energy in vehicles' braking stage to improve energy utilisation. However, the composite onboard energy storage system has several concerns, such as its power and energy demand, battery aging, and maintenance costs. Therefore, the NSGA-II algorithm is proposed to optimise matching the composite energy storage system parameters for urban rail trains. The NSGA-II algorithm was used with an improved elite retention strategy to optimise the parameters matching of the composite power supply. The optimisation's objective concerns the replacement costs and economy of the composite power supply. The method increases the vertical diversity of searching and avoids genetic precocity. The NSGA-II algorithm calls the simulation model of composite power supply in real-time and simultaneously optimises the composite power supply and control parameters. The Pareto set of optimisation objectives and corresponding parameters and control strategies of composite power supply are obtained. The NSGA-II algorithm can optimise the composite energy storage system's parameters and improve the train and the composite power supply's performance indexes. The algorithm greatly reduces the composite power supply's capacitance and reduces the system's total energy consumption. Then, the multi-component energy loss caused by the multi-power source system can be effectively controlled. The total capacitance is reduced by 12.1%, the battery life is prolonged by 18.86%, and the optimised composite power supply's energy storage is increased by 17.6%.

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Section: Multi Objective Optimisationmentioning

confidence: 99%

Model of a Composite Energy Storage System for Urban Rail Trains

Liang¹,

Qi²,

Liang³

2022

Computer Systems Science and Engineering

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“…Programming testing has generally been separated into approval and check (V&V) (Alhroob A. , Alzyadat, Imam, & Jaradat, 2020). Approval is the way toward guaranteeing that the product coordinatestheprerequisites,andcheckguaranteesthattheframeworkmeetstheusefuldetermination buildingoneofthemostwell-knowndefinitionsofapprovalisbuildingtherightframe,andchecking isbuildingtherightframe.Programmingbuildingisloadedwithvulnerability:Asignificantwellspring ofissuesduringprogrammingadvancementisavulnerabilityaboutnecessities;asignificantwellspring of issues during programming development is a vulnerability about plan and execution choices madeduringimprovement (Barstow,1988).Programmingisonesignificantjobwhichisdriving thenumerouselectronicandbusinessitems.Theadvancementoftheseitemsmakesanexpansion intherequirementfortheproduct.Testingisoneimportantperiodofaproductimprovementlife cycle.Scanning errors are performed in programming tools.…”

Section: Background and Related Workmentioning

confidence: 99%

Failure Prediction Approach in Agile Software Development

Alajaleen

Alhroob

2022

International Journal of Software Innovation

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Software failure prediction is an important activity during agile software development as it can help managers to identify the failure modules. Thus, it can reduce the test time, cost and assign testing resources efficiently. RapidMiner Studio9.4 has been used to perform all the required steps from preparing the primary data to visualizing the results and evaluating the outputs, as well as verifying and improving them in a unified environment. Two datasets are used in this work, the results for the first one indicate that the percentage of failure to predict the time used in the test is for all 181 rows, for all test times recorded, is 3% for Mean time between failures (MTBF). Whereas, SVM achieved a 97% success in predicting compared to previous work whose results indicated that the use of Administrative Delay Time (ADT) achieved a statistically significant overall success rate of 93.5%. At the same time, the second dataset result indicates that the percentage of failure to predict the time used is 1.5% for MTBF, SVM achieved 98.5% prediction.

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“…For developing test cases automatically, Search-Based Testing (SBT) is considered. There have been meta-heuristic techniques effectively utilized in the development of test data, including Genetic Algorithms (GA) [3,4], Ant Colony Optimization(ACO) [5,6], Particle Swarm Optimization (PSO) [7,8], Artificial Bee Colony (ABC) [9,10], hybrid Genetic Algorithm [11], Bat Algorithm [12,13]. Fitness functions (FF) drive a metaheuristic algorithm search process.…”

Section: Introductionmentioning

confidence: 99%