Time-travel programming: programming language support for interacting with past executions
Bibliographic record
Abstract
Because software so often behaves unexpectedly or fails only in production environments, several recent tools from both industry and academia record data about execution for the benefit of post-hoc analysis. Debugging on these data instead of a live program is much more difficult, however, because the semantic abstractions provided by the programming language are no longer available. Many post-hoc analysis tools process this data through additional reflection-based code or domain-specific query languages, but do not recover the expressive power of the original programming language. This thesis proposes the concept of time-travel programming, which we define as simulating the execution of additional code in the same programming language as if it were present in the past environment of recorded data. Furthermore, we show that the aspect-oriented programming (AOP) paradigm provides a natural mechanism for specifying this additional execution, and allows us to reuse established semantics and implementations. We provide evidence of this technique’s flexibility, feasibility and effectiveness through two implementations: one an interpreter for an extremely simple AOP language in the style of a core calculus, and one for the AspectJ programming language. We evaluate flexibility via applying the implementations to multiple execution recording formats, feasibility by showing the AspectJ implementation is performant enough for post-hoc analysis, and effectiveness by demonstrating that evaluating new and existing aspects retroactively can be used to address common post-hoc analysis tasks.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".