Facilitating software evolution research with kenyon
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
Software evolution research inherently has several resource-intensive logistical constraints. Archived project artifacts, such as those found in source code repositories and bug tracking systems, are the principal source of input data. Analysis-specific facts, such as commit metadata or the location of design patterns within the code, must be extracted for each change or configuration of interest. The results of this resource-intensive "fact extraction" phase must be stored efficiently, for later use by more experimental types of research tasks, such as algorithm or model refinement. In order to perform any type of software evolution research, each of these logistical issues must be addressed and an implementation to manage it created. In this paper, we introduce Kenyon, a system designed to facilitate software evolution research by providing a common set of solutions to these common logistical problems. We have used Kenyon for processing source code data from 12 systems of varying sizes and domains, archived in 3 different types of software configuration management systems. We present our experiences using Kenyon with these systems, and also describe Kenyon's usage by students in a graduate seminar class.
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 | 0.002 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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