A Call to Promote Soft Skills in Software Engineering
Bibliographic record
Abstract
We have been thinking about other aspects of software engineering for many years; the missing link in engineering software is the soft skills set, essential in the software development process. Although soft skills are among the most important aspects in the creation of software, they are often overlooked by educators and practitioners. One of the main reasons for the oversight is that soft skills are usually related to social and personality factors, i.e., teamwork, motivation, commitment, leadership, multi-culturalism, emotions, interpersonal skills, etc. This editorial is a manifesto declaring the importance of soft skills in software engineering with the intention to draw professionals’ attention to these topics. We have approached this issue by mentioning what we know about the field, what we believe to be evident, and which topics need further investigation. Important references to back up our claims are also included.\nIn summary, technical people tend to overlook the importance of soft skills as it is unrelated to their technical area and because their training is in dealing with technical issue; thus considering the soft skills in the software development process to be foreign to them, since the field deals with human factors and touches social sciences. These are topics that software professionals do not have expertise in. We believe that it is high time for the software development community to realize that the human element is pivotal to success in the engineering of software. We have to recognize that software engineering is a people-intensive discipline, hence requires appropriate treatment. Therefore, human aspects of software engineering are important subjects to teach, study and research. We urge software engineers to take on this challenge.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.023 | 0.055 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".