Test effort estimation of software testing is the complex task. There are multiple factors affect the test effort estimation of software testing. The test effort can be calculated on the basis of effort cost and time required for testing. Multiple studies have been done for developing test effort estimation models but the most of these models provide inaccurate result after some time. . The multiple optimization techniques are used to optimize test effort estimation. The test effort estimation is optimizing multiple model, method and techniques of the test effort estimation. In this paper, we proposed a hybrid algorithm to improve the accuracy. The hybrid algorithm is the combination of firefly and water wave algorithm. The hybrid algorithm is applied on the test effort estimation techniques and the parameters are tuned for optimal performance in terms of minimum error in effort estimation. The results are found to be quite satisfactory both in terms of convergence and accuracy. Thus, it justifies our use of hybrid approach for test effort estimation.

 

Published In : IJCSN Journal Volume 4, Issue 4

Date of Publication : August 2015

Pages : 625 - 631

Figures :08

Tables : 03

Publication Link : A Comparison of Constrain Model Predictive Control and Neural Network for Implementation

 

 

 

Sudhir Sawarkar : Department of Computer Science Engineering, Chandigarh University Mohali , Punjab, India

Gurjot Kaur : Department of Computer Science Engineering, Chandigarh University Mohali , Punjab, India

 

 

 

 

 

 

 

Software Testing

Use Case Point (UCP)

Test Point Analysis (TPA)

Firefly Algorithm

Water Wave Algorithm

Test effort estimation which has become of paramount importance considering the amount of money and resources spent on the software testing field by any company. Test Effort estimation was calculated using two methodologies namely- Use Case Point Analysis and Test Point Analysis. This paper proposes a novel hybrid algorithm comprising of firefly algorithm and water wave algorithm to tune the parameters of effort estimation techniques. The firefly algorithm is utilized for tuning the parameters of use case point analysis and test point analysis separately. But there is an issue with the firefly algorithm. The problem is that the firefly algorithm has itself some tuning parameters which need to be optimized. The performance of the algorithm varies on the values of these parameters. These parameters determine the convergence and accuracy.

 

 

 

 

 

 

 

 

 

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