Managing developer interruption
Bibliographic record
Abstract
The high frequency of interruptions during cognitively-intense activities can be annoying and detrimental to deadline-driven work, such as software development. When developers are interrupted they not only lose productivity from the time spent attending to the interruption but also from the time required to recover from it when they resume working. This thesis provides a solution that addresses the recovery process challenges. It focuses on the recovery of momentum based on the understanding that interruption recovery involves knowledge about the interrupted activity, the developer, as well as the context of the work. We designed FastRecovery, a tool in which data is collected while the developer is working normally. Once an interruption is detected, our tool processes the data assigning scores to each task in order to discover important moments. We propose a set of rules to include the most different types of tasks that a developer can perform. Thereafter, a curated video review is created to mitigate the effects of developer interruptions. A user study was conducted to evaluate the efficacy of our solution. Six participants used the tool for two periods of three days while we collected usage feedback along with self-reported impressions about the tool. The study results were positive and indicate that FastRecovery is beneficial with recovery from unexpected interruptions. Due to the small scale of the study these results are best characterized as an initial indication that our approach is promising with respect to interruption recovery.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".