An Advanced Testing Effort Calculation Algorithm Architecture using GA and Neural Network

An Advanced Testing Effort Calculation Algorithm Architecture using GA and Neural Network

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Vikas Chahar, Pradeep Kumar Bhatia
DOI :  10.14445/22315381/IJETT-V70I5P208

How to Cite?

Vikas Chahar, Pradeep Kumar Bhatia, "An Advanced Testing Effort Calculation Algorithm Architecture using GA and Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 60-73, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P208

Abstract
Early effort estimation holds a significant role when a new project is planned. Otherwise, whenever some changes are appended to the software system during the development phase, they need to be tested again to assure maintenance of software quality. Thus, in both situations, effort estimation plays a key role in completing a project. The paper presents improved software test effort estimations based on similarity analysis and metaheuristics. The designed model considers the effort classification into three test effort classes, namely, low, moderate, and high effort. in the process, the Genetic Algorithm (GA) is integrated for the attribute selection from NASA and Promise datasets used during the evaluation of the proposed model. The three similarity analysis techniques, Cosine, Jaccard, and Euclidean distance, are integrated to find the similarity in individual datasets fed to k-means cluster the data into three clusters. The test effort class prediction performed based on the designed rule set is used to categorize the effort class based on MSE and SE as the validation parameters in the presented work. The simulation analysis performed using two datasets shows the improved test effort predictions by integrating the concept of metaheuristics.

Keywords
Test Effort Estimation, Genetic Algorithm, Cosine Similarity, Jaccard Similarity, Euclidean Distance.

Reference
[1] M. M. Asheeri, and M. Hammad, Machine Learning Models for Software Cost Estimation. in 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ict): Ieee. (2019) 1-6.
[2] M. Dawson, and W. Christian, an Artificial Neural Network Approach to Software Testing Effort Estimation, Wit Transactions on Information and Communication Technologies. 20 (1970).
[3] J. Singh, and B. Sahoo, Software Effort Estimation With Different Artificial Neural Network (2011).
[4] P. R. Srivastava, A. Varshney, P. Nama, and X. S. Yang, Software Test Estimation: A Model Based on Cuckoo Search, International Journal of Bio-Inspired Computation, 4(5) (2012) 278-285.
[5] P. B. Nirpal, and K. V. Kale, Using Genetic Algorithm for Automated, Efficient Software Test Case Generation for Path Testing, International Journal of Advanced Networking and Applications, 2(6) (2011) 911-915.
[6] L. P. Kafle, an Empirical Study on Software Test Effort Estimation, International Journal of Soft Computing and Artificial Intelligence. 2(2) (2014) 109-125.
[7] C. S. Yadav and R. Singh, Tuning of Cocomo Ii Model Parameters for Estimating Software Development Effort Using Ga for Promise Project Data Set, International Journal of Computer Applications. 90(1) (2014) 99-123.
[8] N. Shivakumar, N. Balaji, and K. Ananthakumar, A Neuro-Fuzzy Algorithm to Compute Software Effort Estimation, Global Journal of Computer Science and Technology. 2(1) (2016) 232-243.
[9] R. Saljoughinejad, and V. Khatibi, A New Optimized Hybrid Model Based on Cocomo to Increase The Accuracy of Software Cost Estimation, Journal of Advances in Computer Engineering and Technology, 4(1) (2018) 27-40.
[10] S. K. Sehra, Y. S. Brar, N. Kaur, and S.S. Sehra, Software Effort Estimation Using Fahp and Weighted Kernel Lssvm Machine, Soft Computing. 23(21) (2019) 10881-10900.
[11] V. K. Attri, and J. S. Bal, an Advanced Mechanism for Software Size Estimation Using Combinational Artificial Intelligence, International Journal of Intelligent Engineering and Systems. 12(4) (2019) 32-41.
[12] A. Kumar, B. D. Patro, and B. K. Singh, Parameter Tuning for Software Effort Estimation Using Particle Swarm Optimization Algorithm, International Journal of Applied Engineering Research. 14(2) (2019) 139-144.
[13] S. Chhabra, and H. Singh, Optimizing Design Parameters of Fuzzy-Model-Based Cocomo Using Genetic Algorithms. International Journal of Information Technology, 12(4) (2020a) 1259-1269.
[14] S. Chhabra, and H. Singh. Optimizing The Design of A Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm, International Journal of Computational Intelligence and Applications. 19(01) (2020b) 205-215.
[15] P. Suresh Kumar, and H. S. Behera, Estimating Software Effort Using Neural Network: an Experimental Investigation. in Computational Intelligence in Pattern Recognition. Springer, Singapore. (2020) 165-180
[16] W. Rhmann, B. Pandey, and G. Ansari, Software Effort Estimation Using an Ensemble of Hybrid Search-Based Algorithms Based on Metaheuristic Algorithms, Innovations in Systems and Software Engineering. (2021) 1-11.
[17] N. A. Zakaria, A. R. Ismail, N. Z. Abidin, N.H. M. Khalid, and A. Y. Ali, Optimization of Cocomo Model Using Particle Swarm Optimization, International Journal of Advances in Intelligent Informatics, 7(2) (2021) 177-187.
[18] A. Ardiansyah, R. Ferdinand, and A. E. Permanasari, Mucpso: A Modified Chaotic Particle Swarm Optimization With Uniform Initialization for Optimizing Software Effort Estimation. Applied Sciences. 12(3) (2022) 71-81.
[19] A. Kaushik, N. Singal, and M. Prasad Incorporating Whale Optimization Algorithm With Deep Belief Network for Software Development Effort Estimation, International Journal of System Assurance Engineering and Management, 2(1)(2022) 1-15.
[20] A. Ardiansyah, R. Ferdinand, and A. E. Permanasari, Mucpso: A Modified Chaotic Particle Swarm Optimization With Uniform Initialization for Optimizing Software Effort Estimation, Applied Sciences. 12(3) (2022) 72-81.
[21] D. B. Singh, D. A. Kumar, D. K. Sahni, D. Shree, A. Khushboo, K. Sirohi, D. Khurana. A Model to Measure Software Testing Effort Estimation in The Integrated Environment of Ernn, Bmo & Pso, International Journal of Engineering Trends and Technology. 69(8) 81- 88.
[22] G. Somya, and A. Parashar, Machine Learning Application to Improve Cocomo Model Using Neural Networks, International Journal of Information Technology and Computer Science (Ijitcs) 3 (2018) 35-51.
[23] P. Pandey, and L. Ratnesh, Fuzzy Ahp Based Identification Model for Efficient Application Development, Journal of Intelligent & Fuzzy Systems. 38(3) (2020) 3359-3370.
[24] P. Singal, A. C. Kumari, and P. Sharma, Estimation of Software Development Effort: A Differential Evolution Approach. Procedia Computer Science 167(2020) 2643-2652.
[25] Kaggle`s Online Data Source Available At Https://Www.Kaggle.Com/Sayedmohsin/Sqa-Dataset
[26] A. Kaushik, and N. Singal A Hybrid Model of Wavelet Neural Network and Metaheuristic Algorithm for Software Development Effort Estimation, International Journal of Information Technology. 3(1) (2019) 1-10.
[27] P. Singal, A. C. Kumari, and P. Sharma, Estimation of Software Development Effort: A Differential Evolution Approach, Procedia Computer Science, 167(8) (2020) 2643-2652.
[28] Promise Repository Is Available At Http://Promise.Site.Uottawa.Ca/Serepository/Datasets-Page.Html