With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.

Presence of code smells complicate the source code and can obstruct the development and functionality of the software project. As they represent improper behavior that might have an adverse effect on software maintenance, code smells are behavioral in nature. Python is widely used for various software engineering activities and tends tool to contain code smells that affect its quality. This study investigates five code smells diffused in 20 Python software comprising 10550 classes and analyses its severity index using metric distribution at the class level. Subsequently, a behavioral analysis has been conducted over the considered modification period (phases) for the code smell undergoing class change proneness. Furthermore, it helps to investigate the accurate multinomial classifier for mining the severity index. It witnesses the change in severity at the class level over the modification period by mapping its characteristics over various statistical functions and hypotheses. Our findings reveal that the Cognitive Complexity of code smell is the most severe one. The remaining four smells are centered around the moderate range, having an average severity index value. The results suggest that the J48 algorithm was the accurate multinomial classifier for classifying the severity of code smells with 92.98% accuracy in combination with the AdaBoost method. The findings of our empirical evaluation can be beneficial for the software developers to prioritize the code smells in the pre-refactoring phase and can help manage the code smells in forthcoming releases, subsequently saving ample time and resources spent in the development and maintenance of software projects.