A testing framework for JADE agent-based software
Why this work is in the frame
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Bibliographic record
Abstract
Multi-agent systems are proposed as a solution to mitigate nowadays software requirements: open and distributed architectures with dynamic and adaptive behaviour. Like any other software, multi-agent systems development process is error-prone; thus testing is a key activity to ensure the quality of the developed product. This paper sheds light on agent testing as it is the primary artefact for any multi-agent system’s testing process. A framework called JADE Testing Framework (JTF) for JADE platform’s agent testing is proposed. JTF allows testing agents at two levels: unit (inner-components) and agent (agent interactions) levels. JTF is the result of the integration of two testing solutions: JAT a well-known framework for JADE’s agent’s interaction testing and UJade, a new solution that was developed for agent’s unit testing. UJade provides also a toolbox that allows for enhancing JAT capabilities. The evidence of JTF usability and effectiveness in JADE agent testing was supported by an empirical study conducted on seven multi-agent systems. The results of the study show that: when an agent’s code can be tested either at agent or unit levels UJade is less test’s effort consuming than JAT; JTF provides better testing capabilities and the developed tests are more effective than those developed using UJade or JAT alone.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it