Why do we need personality diversity in software engineering?
Bibliographic record
Abstract
Diversity of skills is good for society, it is also good in problem solving because different people see a problem from several pers-pectives, so diversity should be good for software engineering too. This study tackles a difficult to study aspect of software engineer-ing, that is, how to best associate personnel with the various tasks in a software project. The approach uses psychological types to determine who is best suited to particular development roles. The article has four main objectives: (1) to arouse awareness of human factors among software engineers; (2) to investigate how psycho-logical factors can contribute to their effectiveness at work; (3) to catalyze effort among software engineers leading towards a deeper understanding and broader applications of human factors in the light of the activities involving the engineering of software; and (4) to emphasize the important of skill diversity in the software engi-neering field. This article provides conceptual knowledge, reports findings, and presents both real and hypothesized beliefs from the software engineering community. Likewise, it is hoped that the article will motivate software engineers and psychologists to con-duct more research in the area of software psychology, so as to understand more profoundly the possibilities for increased effec-tiveness and fulfilment among software engineers
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".