An investigation of graph-based class integration test order strategies
Bibliographic record
Abstract
The issue of ordering class integration in the context of integration testing has been discussed by a number of researchers. More specifically, strategies have been proposed to generate a test order while minimizing stubbing. Recent papers have addressed the problem of deriving an integration order in the presence of dependency cycles in the class diagram. Such dependencies represent a practical problem as they make any topological ordering of classes impossible. Three main approaches, aimed at "breaking" cycles, have been proposed. The first one was proposed by Tai and Daniels (1999) and is based on assigning a higher-level order according to aggregation and inheritance relationships and a lower-level order according to associations. The second one was proposed by Le Traon et al. (2000) and is based on identifying strongly connected components in the dependency graph. The third one was proposed by Briand et al. (2000); it combines some of the principles of the two previous approaches and addresses some of their shortcomings (e.g., the first approach may result into unnecessary stubbing whereas the second may lead to breaking cycles by "removing" aggregation or inheritance dependencies, thus leading to complex stubbing). This paper reviews these strategies (principles are described, advantages and drawbacks are precisely investigated) and provides both analytical and empirical comparisons based on five case studies.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".