Task Interruptions in Requirements Engineering: Reality versus\n Perceptions!
Bibliographic record
Abstract
Task switching and interruptions are a daily reality in software development\nprojects: developers switch between Requirements Engineering (RE), coding,\ntesting, daily meetings, and other tasks. Task switching may increase\nproductivity through increased information flow and effective time management.\nHowever, it might also cause a cognitive load to reorient the primary task,\nwhich accounts for the decrease in developers' productivity and increases in\nerrors. This cognitive load is even greater in cases of cognitively demanding\ntasks as the ones typical for RE activities. In this paper, to compare the\nreality of task switching in RE with the perception of developers, we conducted\ntwo studies: (i) a case study analysis on 5,076 recorded tasks of 19 developers\nand (ii) a survey of 25 developers. The results of our retrospective analysis\nshow that in ALL of the cases that the disruptiveness of RE interruptions is\nstatistically different from other software development tasks, RE related tasks\nare more vulnerable to interruptions compared to other task types. Moreover, we\nfound that context switching, the priority of the interrupting task, and the\ninterruption source and timing are key factors that impact RE interruptions. We\nalso provided a set of RE task switching patterns along with recommendations\nfor both practitioners and researchers. While the results of our retrospective\nanalysis show that self-interruptions are more disruptive than external\ninterruptions, developers have different perceptions about the disruptiveness\nof various sources of interruptions.\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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".