Proceedings of the 2016 ACM Workshop on Programming Languages and Analysis for Security
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 11th ACM SIGSAC Workshop on Programming Languages and Analysis for Security (PLAS 2016). For the first time since PLAS began in 2006, PLAS 2016 is co-located with the ACM Conference on Computer and Communications Security (CCS). Over its now ten-year history, PLAS has provided a unique forum for researchers and practitioners to exchange ideas about programming language and program analysis techniques with the goal of improving the security of software systems. This year, PLAS received its third-highest number of submissions, attesting to the continued vitality of the community whose work sits at the intersection of programming languages and security. PLAS has always welcomed the submission of both long research papers as well as short papers presenting preliminary or exploratory work. But, in a slight departure from previous years, the 2016 Call for Papers explicitly solicited short position papers presenting radical, open-ended and forward-looking ideas that are likely to generate lively discussion. The Call for Papers attracted 21 submissions---of which, 10 were short papers---from 13 countries (Australia, Belgium, Canada, Czech Republic, Denmark, Estonia, France, Germany, India, Italy, Romania, Sweden, USA), with authors spanning both academia and industry. PLAS 2016 is delighted to have two excellent invited talks: Flow: Analysis of JavaScript for type checking and beyond, Avik Chaudhuri (Facebook)Verified Secure Implementations for the HTTPS Ecosystem, Cédric Fournet (Microsoft Research)
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.060 | 0.025 |
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".