Beyond the Lone Reverse Engineer: Insourcing, Outsourcing and Crowdsourcing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it