Implications of Resurgence in Artificial Intelligence for Research Collaborations in Software Engineering
Bibliographic record
Abstract
Challenges of implementing successful research collaborations between industry and academia in software engineering are varied and many. Differing timelines, metrics, expectations, and perceptions of these two communities are some common obstacles, which need be analyzed and discussed, to discover synergies and strengthen collaborations between researchers and practitioners. In this report, we present insights from the 6th International Workshop on Software Engineering Research and Industrial Practice held at the International Conference on Software Engineering 2019. Specifically, one particular topic dominated the discussion - the resurgence of artificial intelligence and machine learning algorithms in software engineering research and industry practice, and its implications for the collaboration between these two communities. We present takeaways from keynote talks on this subject, insights from paper presentations, and findings from the discussion session.
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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 | 0.202 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.029 | 0.049 |
| Scholarly communication | 0.039 | 0.062 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".