RETRACTED ARTICLE: Usability feature extraction using modified crow search algorithm: a novel approach

• Published in: Neural computing & applications Vol. 32; no. 15; pp. 10915 - 10925
• Main Authors: Gupta, Deepak, Rodrigues, Joel J. P. C., Sundaram, Shirsh, Khanna, Ashish, Korotaev, Valery, de Albuquerque, Victor Hugo C.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Probability and Statistics in Computer Science; S.I. : Computer aided Medical Diagnosis; Feature extraction; Crow search algorithm; Modified crow search algorithm; Software quality; HCI
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3688-6

For the purpose of usability feature extraction and prediction, an innovative metaheuristic algorithm is introduced. Generally, the term “usability” is defined by the several researchers with respect to the hierarchical-based software usability model and it has become one of the important methods in terms of software quality. In hierarchically based software, its usability factors, attributes, and its characteristics are combined. The paper presented an algorithm, i.e., modified crow search algorithm (MCSA) mainly for extraction of usability features from hierarchical model with the optimal solution under the search for useful features. MCSA is an extension of original crow search algorithm (CSA), which is a naturally inspired algorithm. The mechanism of this algorithm is based on the process of hiding food and prevents theft and hence introduced this CSA in the field of software engineering practices as an inspiration. The algorithm generates a particular number of selected features/attributes and is applied on software development life cycles models, finding out the best among them. The results of the presented algorithm are compared with the standard binary bat algorithm (BBA), original CSA, and modified whale optimization algorithm (MWOA). The outcomes conclude that the proposed MCSA performs well than the standard BBA and original CSA as the proposed algorithms generate fewer number of feature selection equal to 17 than 18 in BBA, 23 in CSA, and 19 in MWOA.