An Advanced Testing Effort Calculation Algorithm Architecture using GA and Neural Network
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Vikas Chahar, Pradeep Kumar Bhatia
|DOI : 10.14445/22315381/IJETT-V70I5P208|
MLA Style: Vikas Chahar, and 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, May. 2022, pp. 60-73. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P208
APA Style:Vikas Chahar, & Pradeep Kumar Bhatia. (2022). An Advanced Testing Effort Calculation Algorithm Architecture using GA and Neural Network. International Journal of Engineering Trends and Technology, 70(5), 60-73. https://doi.org/10.14445/22315381/IJETT-V70I5P208
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.
Test Effort Estimation, Genetic Algorithm, Cosine Similarity, Jaccard Similarity, Euclidean Distance.
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