Analyzing Program Dependencies in Java EE Applications
Bibliographic record
Abstract
Program dependency artifacts such as call graphs help support a number of software engineering tasks such as software mining, program understanding, debugging, feature location, software maintenance and evolution. Java Enterprise Edition (JEE) applications represent a significant part of the recent legacy applications, and we are interested in modernizing them. This modernization involves, among other things, analyzing dependencies between their various components/tiers. JEE applications tend to be multilanguage, rely on JEE container services, and make extensive use of late binding techniques-all of which makes finding such dependencies difficult. In this paper, we describe some of these difficulties and how we addressed them to build a dependency call graph. We developed our tool called DeJEE (Dependencies in JEE) as an Eclipse plug-in. We applied DeJEE on two open-source JEE applications: Java PetStore and JSP Blog. The results show that DeJEE is able to identify different types of JEE dependencies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".