Investigating the use of analysis contracts to improve the testability of object‐oriented code
Bibliographic record
Abstract
Abstract A number of activities involved in testing software are known to be difficult and time consuming. Among them is the definition and coding of test oracles and the isolation of faults once failures have been detected. Through a thorough and rigorous empirical study, we investigate how the instrumentation of contracts could address both issues. Contracts are known to be a useful technique in specifying the precondition and postcondition of operations and class invariants, thus making the definition of object‐oriented analysis or design elements more precise. It is one of the reasons the Object Constraint Language (OCL) was made part of the Unified Modeling Language. Our aim in this paper is to reuse and instrument contracts to ease testing. A thorough case study is run where we define OCL contracts, instrument them using a commercial tool and assess the benefits and limitations of doing so to support the automated detection of failures and the isolation of faults. As contracts can be defined at various levels of detail, we also investigate the cost and benefit of using contracts at different levels of precision. We then draw practical conclusions regarding the applicability of the approach and its limitations. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.022 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".