Investigating java type analyses for the receiver-classes testing criterion
Bibliographic record
Abstract
This paper investigates the precision of three linear-complexity type analyses for Java software: Class Hierarchy Analysis (CHA), Rapid Type Analysis (RTA) and Variable Type Analysis (VTA). Precision is measured relative to class targets. Class targets results are useful in the context of the receiver-classes criterion, which is an object-oriented testing strategy that aims to exercise every possible class binding of the receiver object reference at each dynamic call site. In this context, using a more precise analysis decreases the number of infeasible bindings to cover, thus it reduces the time spent on conceiving test data sets. This paper also introduces two novel variations to VTA, called the iteration and intersection variants. We present experimental results about the precision of CHA, RTA and VTA on a set of 17 Java programs, corresponding to a total of 600 kLOC of source code. Results show that, on average, RTA suggests 13% less bindings than CHA, standard VTA suggests 23% less bindings than CHAt and VTA with the two variations together suggests 32% less bindings than CHA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".