scholarly journals A Hybrid Approach for Test Case Prioritization using PSO Based on Software Quality Metrics

2018 ◽  
Vol 7 (3.12) ◽  
pp. 300
Author(s):  
K Senthil Kumar ◽  
A Muthukumaravel
Keyword(s):  
Software Quality ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Software Application ◽  
Multiple Test ◽  
Software Quality Metrics

Effective functionality checking of any software application is the crucial event that determines the quality of outcome obtained.  Generally, checking scenarios that involves multiple test cases in mixture with multiple components is time consuming and also increases the quality assurance cost. Selection of suitable method/approach for optimization and prioritization of test cases as well as appropriate evaluation of the application would result in reduction of fault detection effort without appreciable information loss and further would also significantly decrease the clearing up cost. In the proposed method, test cases are optimized and then prioritized by Particle Swarm Optimization algorithm (PSO) and Improved Cuckoo Search algorithm (ICSA), respectively. Finally, the result will be evaluated for software quality measures. 

scholarly journals Cuckoo Search Algorithm for Test Case Prioritization in Regression Testing

2019 ◽  
Vol 8 (3) ◽  
pp. 6004-6009
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Aggressive Approach ◽  
Traditional Approaches

There are countless optimization problems that have been accelerated by Nature Inspired Metaheuristic Optimization Algorithms (NIMOA) in the earlier decades. NIMOA have gained huge popularity owing to their effective results. In this study NIMOA namely, Cuckoo Search Algorithm (CSA) is used to prioritize (order) the test cases for Regression Testing (RT). Prioritizations aids in the execution of higher priority test cases to give early fault detection. This research adopts the aggressive approach of reproduction made by Cuckoos to prioritize the test cases for RT. Average Percentage of Fault Detected (APFD) metrics is used in this paper for validations of results. APFD metrics is used to compare the performance of CSA with Flower Pollination Algorithm (FPA) and traditional approaches for Test Case Prioritization (TCP). Two java applications are used for the study. CSA is implemented in Java on eclipse platform. It is learnt from the study that APFD results of CSA outperformed the FPA for both the applications namely Puzzle Game and AreaandPerimeter. It is inferred from the results that prioritized set of test cases given by NIMOA outperformed the APFD results of traditional approaches and also CSA performed better than the FPA for TCP.

Test Case Prioritization using Cuckoo Search

2012 ◽  
pp. 113-128 ◽  
Author(s):  
Praveen Ranjan Srivastava ◽  
D. V. Pavan Kumar Reddy ◽  
M. Srikanth Reddy ◽  
Ch. V. B. Ramaraju ◽  
I. Ch. Manikanta Nath
Keyword(s):  
Software Maintenance ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Specific Sequence ◽  
Scarcity Of Resources

Test Case prioritization consists of proper organization and scheduling of the test cases in a specific sequence. Regression testing is an important issue and concept during software maintenance process, but due to scarcity of resources re-execution of all test cases, is not possible during regression testing. Hence in version or revision specific regression testing, it is more important to execute those test cases that are beneficial. In this chapter, a new prioritization technique is proposed for version specific regression testing using Cuckoo Search Algorithm. This technique prioritizes the test cases based on lines of code where the code is modified.

scholarly journals Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2953-2964
Keyword(s):  
Data Flow ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Automatic Generation ◽  
Cuckoo Search Algorithm ◽  
Test Case ◽  
Stochastic Algorithm ◽  
Data Flow Testing ◽  
Work Done

Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.

scholarly journals Remote Sensing Imagery Segmentation: A Hybrid Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 4604
Author(s):  
Shreya Pare ◽  
Himanshu Mittal ◽  
Mohammad Sajid ◽  
Jagdish Chand Bansal ◽  
Amit Saxena ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parameter Analysis ◽  
Energy Curve ◽  
Remote Sensing Imagery ◽  
Renyi’S Entropy

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

scholarly journals The use of Metaheuristic Algorithms in Early Prediction and Forecasting of Flood – A Use of Cuckoo Search Algorithm based Optimization for Flood Controlling

2020 ◽  
Vol 9 (2) ◽  
pp. 139-143
Keyword(s):  
Flood Control ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Early Prediction ◽  
Neural Network Models

Flood is one of the disasters which have multiple impacts on the society and industry. It has severe impacts on the urban economy and has forced the scholars to develop resiliency plans. Various types of flood forecasting techniques developed by the scholars and have certain limitations. There are various types of multiple modeling techniques which are being used for flood controlling and each has certain limitations. The optimization techniques along with the artificial intelligence algorithms can be helpful for monitoring and early prediction of flood. The neural network models promises better accuracy compared to convention models for prediction, but they face great difficulties in selection of appropriate model parameters. In the said context, here an effort has been made to explore the importance of Cuckoo theorem in flood management. The cuckoo search algorithm can be used for parameter tuning. The hybrid approach of using cuckoo search algorithm with neural networks has given far better accuracy compared to standalone algorithms. The use of such Cuckoo Search Metaheuristic algorithm will help us to predict early warning system than any other method and helps us to align the flood controlling activities. The paper presents the used of variants of cuckoo search algorithm for early flood prediction. The paper unfolds major insights of flood scenarios along with the significance of flood control and monitoring.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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