Beyond the Lone Reverse Engineer: Insourcing, Outsourcing and Crowdsourcing
Bibliographic record
Abstract
When one imagines a reverse engineer at work, an image that often comes to mind is that of a lone engineer using advanced tools to help in design recovery. However, in practice the engineer may be part of a team that has to tackle the arduous task of documenting a system's design. Often, such a team will be distributed and may have to work in an asynchronous manner. Moreover, sharing and combining knowledge will transient or non-team members further adds to the complexity of the task. These collaboration challenges are seldom discussed or even mentioned in the research literature. In this talk, I will explore how models, theories and technologies from the disciplines of computer supported cooperative work and social computing can improve and encourage collaboration in reverse engineering. I will briefly present several success stories on how social computing technologies have helped improve how small teams, distributed larger teams and the crowd tackle complex intellectual tasks in other areas of science. I will also describe some of our early work investigating how Web 2.0 social computing technologies, such as tagging and feeds facilitate collaborative software engineering. My hope is that these stories may spark ideas on how social computing might inspire new research in reverse engineering.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 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".