Towards LLM-Assisted System Testing for Microservices
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
As modern applications are being designed in a distributed, Microservices Architecture (MSA), it becomes increasingly difficult to debug and test those systems. Typically, it is the role of software testing engineers or Quality Assurance (QA) engineers to write software tests to ensure the reliability of applications, but such a task can be labor-intensive and time-consuming. In this paper, we explore the potential of Large Language Models (LLMs) in assisting software engineers in generating test cases for software systems, with a particular focus on performing end-to-end (black-box) system testing on web-based MSA applications. We present our experience building Kashef, a software testing tool that utilizes the advanced capabilities of current LLMs in code generation and reasoning, and builds on top of the concept of communicative agents.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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