A Comparative Study of Invariants Generated by Daikon and User-Defined Design Contracts
Bibliographic record
Abstract
A lot of progress has been made towards reverse-engineering program specification under the form of contracts. Ensuring the quality of such reverse-engineered contracts, referred to as likely invariants when using Daikon, is paramount since those contracts are used in several other contexts. One aspect that can influence the "quality" of the reverse-engineered contracts is the configuration being used when executing Daikon. In this paper we evaluate the impact of two such configuration parameters which help the user control in two different ways how many variables of the program are considered by Daikon when inferring likely invariants. We perform a case study with a program equipped with test cases and high-level design contracts (i.e., design contracts produced before implementation) and systematically compare likely invariants reverse-engineered by Daikon to those contracts, thanks to a comparison framework we devised. Results confirm and complement previous works, whereby we show that: a good proportion of design contracts are correctly identified by Daikon as likely invariants, many design contracts are not discovered by Daikon, looking for design contract in the set of likely invariants amounts to searching for a needle in a haystack. Our experiment also allows us to suggest a cost-effective configuration when running Daikon.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".