Analyzing Competences in Software Testing: Combining Thematic Analysis with Natural Language Processing (NLP)
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".