Accelerating Software Engineering Research Adoption with Analysis Bots
Bibliographic record
Abstract
An important part of software engineering (SE) research is to develop new analysis techniques and to integrate these techniques into software development practice. However, since access to developers is non-trivial and research tool adoption is slow, new analyses are typically evaluated as follows: a prototype tool that embeds the analysis is implemented, a set of projects is identified, their revisions are selected, and the tool is run in a controlled environment, rarely involving the developers of the software. As a result, research artifacts are brittle and it is unclear if an analysis tool would actually be adopted. In this paper, we envision harnessing the rich interfaces provided by popular social coding platforms for automated deployment and evaluation of SE research analysis. We propose that SE analyses can be deployed as analysis bots. We focus on two specific benefits of such an approach: (1) analysis bots can help evaluate analysis techniques in a less controlled, and more realistic context, and (2) analysis bots provide an interface for developers to "subscribe" to new research techniques without needing to trust the implementation, the developer of the new tool, or to install the analysis tool locally. We outline basic requirements for an analysis bots platform, and present research challenges that would need to be resolved for bots to flourish.
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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 | 0.062 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".