Charting a Path to Efficient Onboarding: The Role of Software Visualization
Bibliographic record
Abstract
Background. Within the software industry, it is commonly estimated that software professionals invest a substantial portion of their work hours in the process of understanding existing systems. In this context, an ineffective technical onboarding process, which introduces newcomers to software under development, can result in a prolonged period for them to absorb the necessary knowledge required to become productive in their roles. Goal. The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools and how these tools are employed to facilitate the technical onboarding of new team members. Method. To address the research problem, we built upon the insights gained through the literature and embraced a sequential exploratory approach. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews. Findings. Our findings demonstrate a gap between the concept of software visualization and the practical use of onboarding tools and techniques. Overall, practitioners do not systematically incorporate software visualization tools into their technical onboarding processes due to a lack of conceptual understanding and awareness of their potential benefits. Conclusion. The software industry could benefit from standardized and evolving onboarding models, improved by incorporating software visualization techniques and tools to support program comprehension of newcomers in the software projects.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".