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ARXIV:2605.14202 · LLM FOR SOFTWARE TESTING · SUBMITTED 15 MAY · 20:13 UTC · FRESHNESS FRESH
ARXIV:2605.14202LLM FOR SOFTWARE TESTINGSUBMITTED 15 MAY · 20:13 UTCFRESHNESS FRESHHrushitha Goud Tigulla · Marco Vieira · arXiv
An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications.
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Pain An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications.
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An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests…
Malformed, missing, or boundary-value inputs in microservice APIs can cascade across dependent services, threatening reliability. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests remains challenging.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We find that prompt strategy explains more variation in diversity than model size: a Structured prompt collapses diversity entirely, while a single model varied…
LLM for Software Testing moved forward this cycle; last verified May 2026. Public score 4.0/10.
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An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications.
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10.48550/arXiv.2605.14202An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications.
Abstract
Malformed, missing, or boundary-value inputs in microservice APIs can cascade across dependent services, threatening reliability. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests remains challenging. Large Language Models can generate such tests from API specifications; however, it is unknown whether different models and prompt strategies produce diverse failure sets or converge on the same failures. We report a controlled experiment applying 7 prompt strategies to 3 open-source LLMs (14B-70B parameters) targeting 2 architecturally distinct microservice systems: one Java monolingual (6 services, 9 failure modes) and one polyglot (27 services, 14 failure modes), yielding 38 valid runs and 663 generated tests. We find that prompt strategy explains more variation in diversity than model size: a Structured prompt collapses diversity entirely, while a single model varied across three prompt strategies achieves complete failure-mode coverage on one system, outperforming any multi-model ensemble under a fixed prompt. We introduce two strategies, Guided and GuidedFewShot, that embed a mutation taxonomy from prior robustness testing research as domain context. GuidedFewShot achieves the highest single-run coverage on both systems (5 of 9 and 8 of 14 failure modes) while maintaining low cross-model similarity. A key lesson is that taxonomy rules alone are insufficient: LLMs cannot distinguish key-absent from value-empty mutations without concrete examples. Findings replicate across both systems.
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PROBLEM
An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests r...
METHOD
Malformed, missing, or boundary-value inputs in microservice APIs can cascade across dependent services, threatening reliability. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests remains challenging.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We find that prompt strategy explains more variation in diversity than model size: a Structured prompt collapses diversity entirely, while a single model varied across three prompt strategies achieves com...
WHY NOW
LLM for Software Testing moved forward this cycle; last verified May 2026. Public score 4.0/10.
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An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Malformed, missing, or boundary-value inputs in microservice APIs can cascade across dependent services, threatening reliability. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We find that prompt strategy explains more variation in diversity than model size: a Structured prompt collapses diversity entirely, while a single model varied across three prompt strategies achieves complete failure-mode coverage on one system, outperforming any multi-model ensemble under a fixed prompt.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM for Software Testing moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An empirical study investigating the effectiveness of different LLM prompt strategies for generating robust test cases for microservice applications.
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LLM for Software Testing
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