Static analysis to support the evolution of exception structure in object-oriented systems
Bibliographic record
Abstract
Exception-handling mechanisms in modern programming languages provide a means to help software developers build robust applications by separating the normal control flow of a program from the control flow of the program under exceptional situations. Separating the exceptional structure from the code associated with normal operations bears some consequences. One consequence is that developers wishing to improve the robustness of a program must figure out which exceptions, if any, can flow to a point in the program. Unfortunately, in large programs, this exceptional control flow can be difficult, if not impossible, to determine.In this article, we present a model that encapsulates the minimal concepts necessary for a developer to determine exception flow for object-oriented languages that define exceptions as objects. Using these concepts, we describe why exception-flow information is needed to build and evolve robust programs. We then describe Jex, a static analysis tool we have developed to provide exception-flow information for Java systems based on this model. The Jex tool provides a view of the actual exception types that might arise at different program points and of the handlers that are present. Use of this tool on a collection of Java library and application source code demonstrates that the approach can be helpful to support both local and global improvements to the exception-handling structure of a system.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".