Abstract In terms of training students for work in diverse firms, traditional and out‐of‐date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must be changed. An Educational Assistant for Software Testing (EAST) framework is developed in this work to train students to improve their skills in software testing via Computer Assisted Instruction (CAI) built using Natural Language Processing (NLP), Machine learning, and information retrieval techniques. In this paper, a Group Search Optimized two‐stage hybrid Support Vector Machine‐K‐Nearest Neighbor (SVM‐KNN) classifier is used to develop a novel approach for analyzing the parameters that introduce bugs in bug reports. To decrease the data sparsity problem, the group search optimization (GSO) algorithm is used to improve the parameter selection process of the two‐stage hybrid classifier by generating optimal values for parameters such as k, c, and gamma. Two bug report datasets were used to test the model. The database for our application is built by collecting bug reports from a wide open‐source community as well as several mobile application development companies. Based on the extensive experiments conducted via different performance metrics, we can conclude that the EAST framework can improve outdated teaching methodologies.

Abstract In mobile ad hoc network, the routing task is considered to be more complicated issue. In order to find efficient route in MANET, the positions of each stable node in the network is identified initially. The main contribution of this paper is to identify the locations of neighboring nodes in MANET for the establishment of multi path routing in diverse mobility patterns. It also handles packet scheduling to balance the load as well as data forwarding with less communication time. The proposed work explains four phases such as stable node prediction, stability determination, route exploration, and packet dissemination. At first, the stable nodes are identified using recurrent neural network along with modified seagull optimization approach. By means of Garson's pruning based recurrent neural network accompanied with modified sea gull optimization (RMSG) algorithm, the stable neighbors are chosen. The network routing is formed by interconnecting the stable nodes from the source to the destination. When a routing link failure happens, the route recovery process will be initiated. Thus, the data packets are broadcasted in multi‐paths without any intervention. The measures namely packet delivery ratio, throughput, end‐to‐end delay, routing overhead, optimal path selection, and energy consumption are utilized to evaluate the performance of proposed approach. The experimental analysis proved that the proposed approach well performed than other compared existing approaches.