System-level Usage Dependency Analysis of Object-Oriented Systems
Bibliographic record
Abstract
Uncovering, modelling, and understanding architectural level dependencies of software systems is a key task for software maintainers. However, current dependency analysis techniques for object-oriented software are targeted at the class or method level; this is because most dependencies—such as instantiates, references, and calls—must be interpreted in the context of one or more class hierarchies. In this paper, we propose an approach, called the High-level Object Dependency Graph (HODG), that captures all possible usage dependencies among coarse-grained entities. Based on the new model, we further propose a set of dependency analysis methods. Finally, we present an exploratory case study using HODGs—supported by an automated analysis tool—of the Apache Ant build system; we show how HODG analysis can help maintainers capture external properties of coarse-grained entities, and better understand the nature of their interdependencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".