The Process of Research and Development in Software Quality Management Based on Text Mining

hasan asil¹

IOI Code : ABEB-ACACAC
Publication : 4th International Conference on Recent Advances in Engineering, Innovation and Technology - Belgium(eitconf.com)
Abstract :
    With the exponential growth of scientific publications in software engineering, tracking and monitoring research trends has become increasingly challenging for researchers. This study aims to investigate the process of research and development in Software Quality Management (SQM) by employing text mining and information clustering techniques. To this end, approximately 10,000 articles published in the ScienceDirect database over the past five years were extracted. Following data preprocessing and cleaning, the papers were analyzed using the K-means clustering algorithm and keyword analysis (evaluating a total of nearly 17,000 keywords). The results indicate that topics such as “Data Quality” and “Machine Learning” played a pivotal role in 2016, while concepts related to “Quality” and “Agile Development Quality” emerged as the most frequently used research areas. This study demonstrates that text mining serves as an efficient tool for technology tracking and identifying future research directions in software quality management.

Keywords : text mining software quality management clustering research trend analysis machine learning