AIRE 2023: 10th International Workshop on Artificial Intelligence and Requirements Engineering
Bibliographic record
Abstract
Requirements Engineering (RE) researchers have employed Artificial Intelligence (AI) techniques to tackle different notions of requirements quality, have applied the techniques to different case studies and domains, and have used different metrics to assess the performance of their techniques. Given the pervasiveness of AI-based systems in our daily life, recent years have also seen an increasing need for RE techniques to support sound and structured development of AI system, with particular interest in explainability of system behaviour. The primary purpose of the AIRE workshop is to explore synergies between AI and RE in order to identify complex RE problems that could benefit from the application of AI techniques and the other way round, thus addressing RE for AI challenges. The 2023 edition of the workshop received 14 submissions, which were independently reviewed by at least three program committee members. In the end, 9 papers were accepted. All the conflicts of interest were treated seriously and independently. The workshop takes place on September 5, 2023. We hope that you enjoy the AIRE'23 workshop and its proceedings. We believe that in the days when AI is gaining prominence in our daily lives, the RE community cannot neglect the benefits that AI techniques can deliver to the practice of requirements engineering. The workshop will feature a keynote by Dr. Alessio Ferrari from CNR-ISTI (Italy) on Artificial Intelligence in Engineering and Society: Blue Skies, Black Holes, and the Job of Requirements Engineers. We look forward to seeing you all at this workshop and the future editions. We are very grateful to the Program Committee members and authors of the submissions for their hard work and dedication in putting together this program. We would like to thank you all for your participation in AIRE'23.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.038 |
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".