Understanding what industry wants from requirements engineers
Bibliographic record
Abstract
[Background] Prior research on the professional occupation of Requirements Engineering (RE) in Europe and Latin America indicated incongruities between RE practice as perceived by industry and as in textbooks, and conducted detailed analysis of both RE and non-RE job aspects. Relatively little is published on the RE competencies and skills industry expects, and seldom investigated the application domains calling for RE professionals. [Aims] We felt motivated by those findings to carry out research on RE job posts in a North-American market. Especially, we focused solely on RE-specific tasks, competencies and skills, from the perspective of defined position categories. Plus, we intend to explore the application domains in need for RE professionals to reveal the wide range of RE roles in industry. [Methods] Coding process, analysis, and synthesis were applied to the textual descriptions of the 190 RE job ads from Canada's most popular online job search site, especially to the text referring to tasks and competencies. [Results] We contribute to the empirical analysis of RE jobs, by providing insights from Canada's IT market in 2017. Using 109 RE job ads from the most popular IT job search portal T-Net, we identified the qualifications, experience and skills demanded by Canadian employers. Furthermore, we explored the distribution of those RE tasks and competences over the 11 categories of RE roles. [Conclusions] Our results suggest that the majority of the employers were big to very big companies in 29 business domains, and the most in-demand RE skills for them were related to RE methods and to project management aspects affecting requirements. In addition, employers placed much more emphasis on experience - both RE-specific and broad software engineering experience, than on higher education.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".