Reframing the Liskov substitution principle through the lens of testing
Bibliographic record
Abstract
In this essay, we explore a new pedagogical framing ofway of pedagogically and teaching the Liskov Substitution Principle (LSP). In addition to, or perhaps even in place of, teaching the specifics of the rule itself, we advocatepropose teaching an operationalised version of the rule: that a subtype must pass its supertype’s black box tests for each of its overriding methods. We leverage the fact that black box tests should be written to capture conformance to a specification without overfitting or checking implementation internalsdetails (as would be checked by glass box tests). A type that violates the rules of substitutability will also fail a potential corresponding black box test for the supertype. Additionally, we argue that the over-strict nature of the classical LSP Postcondition Rule (which has been improved in subsequent work) can be a source of confusion for both instructors and for students learning this crucial concept for the first time. Pleasingly, many of the technical subtleties of this nuanced but important concept drop out naturally when thinking of substitutability via black box tests. Thus we put forward We propose that this test-oriented means of teaching substitutability is a valuable alternative to the classical sense of checking the LSP, with the benefit of being intuitively accessible to students.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".