REVIEW OF EFFECTIVE CANDIDATE EVALUATION USING KSA PARAMETERS

Abstract

This literature study has the goal of reviewing the significance of Knowledge, Skills, and Abilities in resume analysis in the case of software engineering applicants. The period of study is from 2015 to 2024, and the emphasis is on the use of Natural Language Processing (NLP) and Machine Learning (ML) in the automation of the recruitment process. The purpose of the study is to assess KSA(Knowledge, Skills, Abilities) factors in their relationship to resume analysis and evaluate successful approaches in the application of NLP and ML. Research data was obtained through academic databases. Inclusion criteria included information on KSA, peer-reviewed studies, and data on the NLP and ML application in resume analysis. The result is that 58 records were selected and submitted to risk of bias evaluation. The findings state that the employment of the combined NLP and ML significantly assists in the process of KSA evaluation of submitted resumes. Recommendations include further studies of the analysis and information extraction skills of the two technologies. The implications of KSA factors are that they significantly improve the resume analysis and candidate assessment. The results present important stakeholders, most influential researchers and authors, most reliable journals, and major trends in the field of resume evaluation. This study constitutes a new basis for the following research and applications. The emphasis can be made on the utilization of standardized concepts for KSA evaluation and further innovation in this sphere.