Bibliographic record
Abstract
The evolution of generative AI holds tremendous promise for automating and enhancing various facets of software engineering.However, as with any nascent technology, it comes with its own set of challenges.A notable one is that generative AI can occasionally 'hallucinate', producing code that, while syntactically correct, may be contextually off-mark or even erroneous.These instances underscore the urgent need for refined methodologies and deeper insights.In this keynote, Prof. Mesbah delves into the confluence of program analysis and generative AI, charting a course toward more precise and context-aware code generation.He posits that instead of arbitrarily choosing context for a developer task, the context should encapsulate meaningful and pertinent details relevant to the task.By showcasing key findings from his recent research, he will highlight how a software's contextual grasp can be pivotal in enhancing the accuracy of AI-driven code generation.Journey with us into a session that reveals how the fusion of program analysis with generative AI is setting the stage for a future where technology seamlessly augments human creativity and precision in coding activities. Bio
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.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.290 | 0.153 |
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".