Structured Acceptance Test Suite Generation Process for Multi-Agent System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, Agent-Oriented Software Engineering (AOSE) methodologies are proposed to develop complex distributed systems based upon the agent paradigm. The implementation for such systems has usually the form of Multi-Agent Systems (MAS). Testing of MAS is a challenging task because these systems are often programmed to be autonomous and deliberative, and they operate in an open world, which requires context awareness. In this paper, we introduce a novel approach for goal-oriented software acceptance testing. It specifies a testing process that complements the goal oriented methodology Tropos and strengthens the mutual relationship between goal analysis and testing. Furthermore, it defines a structured and comprehensive acceptance testing process for engineering software agents by providing a systematic way of deriving test cases from goal analysis.
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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 | 0.000 |
| 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 | 0.009 |
| 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 it