A Visual Narrative Path from Switching to Resuming a Requirements\n Engineering Task
Bibliographic record
Abstract
Requirements Engineering (RE) is closely tied to other development activities\nand is at the heart and foundation of every software development process. This\nmakes RE the most data and communication-intensive activity compared to other\ndevelopment tasks. The highly demanding communication makes task switching and\ninterruptions inevitable in RE activities. While task switching often allows us\nto perform tasks effectively, it imposes a cognitive load and can be\ndetrimental to the primary task, particularly in complex tasks as the ones\ntypical for RE activities. Visualization mechanisms enhanced with analytical\nmethods and interaction techniques help software developers obtain a better\ncognitive understanding of the complexity of RE decisions, leading to timelier\nand higher quality decisions. In this paper, we propose to apply interactive\nvisual analytics techniques for managing requirements decisions from various\nperspectives, including stakeholders communication, RE task switching, and\ninterruptions. We propose a new layered visualization framework that supports\nthe analytical reasoning process of task switching. This framework consists of\nboth data analysis and visualization layers. The visual layers offer\ninteractive knowledge visualization components for managing task interruption\ndecisions at different stages of an interruption (i.e. before, during, and\nafter). The analytical layers provide narrative knowledge about the\nconsequences of task switching decisions and help requirements engineers to\nrecall their reasoning process and decisions upon resuming a task. Moreover, we\nsurveyed 53 software developers to test our visual prototype and to explore\nmore required features for the visual and analytical layers of our framework.\n
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".