Analyzing Competences in Software Testing: Combining Thematic Analysis with Natural Language Processing (NLP)

Abstract

This Full Paper (Research) presents an analysis on the competences in software testing for the fresh graduates in computer science. Software Testing education (ST) is receiving increasing attention in literature, recent studies have evaluated instructional methods used in ST education. However, analysis of competences (skills, knowledge, and ability) required in ST education are lacking in literature. Competences play critical roles in curriculum development e.g., they inform the design of student learning outcomes, learning objectives and program outcomes. This full paper in the research category aims to analyze competences in ST education and then examine the gap between these competences and the current ST curriculum. Using natural language processing (NLP) techniques, we collect 2033 job descriptions from three popular job portals (indeed, monster, and career builder) in the USA and Canada. Also, we collected course syllabi from 20 universities offering ST courses and use these to assess the current curriculum in ST. We analyzed the data using thematic analysis and found that the current software testing curricula do not always teach or equip students with some of the soft skills they require to be successful in software testing career. For instance, our result shows that soft skills such as teamwork, communication, leadership, which are often required by software testing employers are not always taught in ST courses.