FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs

• URL: http://arxiv.org/abs/2608.26746v1
• Date: Thu, 27 Aug 2026 07:37:28 GMT
• Title: FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs
• Abstract summary: FaultLens is a method for learning compact behavioral test suites while preserving an auditable connection to executed evidence.<n>It executes a rich probe domain once, stores the fault-probe kill relation as a sparse outcome cache, and learns probe orderings only from earlier program generations.<n>We evaluate twenty generated operational policies across four environments, ten execution seeds, 1,200 measured run summaries, 2,160 controlled program transformations, and 4,120,200 executed program-probe pairs.
• Score: 13.55424508650495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generated operational programs are often validated with either a few hand-written examples or exhaustive regression suites. The former can miss sparse boundary and interaction faults, while the latter can be unnecessarily expensive. We introduce FaultLens, a method for learning compact behavioral test suites while preserving an auditable connection to executed evidence. It executes a rich probe domain once, stores the fault-probe kill relation as a sparse outcome cache, and learns probe orderings only from earlier program generations. A fault-driven greedy component exploits known kill structure, while a mutation-independent diversity component covers probe families, cases, templates, and temporal bins. Their alternating hybrid remains useful when a new program contains a fault mechanism absent from ordering construction. We evaluate twenty generated operational policies across four environments, ten execution seeds, 1,200 measured run summaries, 2,160 controlled program transformations, and 4,120,200 executed program-probe pairs. Of 1,960 intended faulty transformations, 1,779 alter a contract or output somewhere in the finite audit domain; 200 additional controls preserve behavior. A 32-probe hybrid learned on generations 1-3 covers 576/582 (99.0%) dynamically killable faults in generations 4-5 using 1.2-2.0% of the exhaustive domain. With an entire fault family withheld from training, diversity raises scenario-family macro coverage from 84.6% to 94.9%. In a downstream deployment study, a conservative admission rule reduces severe tail regressions from 15/20 program-environment groups to 0/20. FaultLens provides a prioritized evidence mechanism, not a proof of correctness, and makes its budget, evidence source, generalization split, and misses explicit.

Related papers

• Guardrailed Meta-Agent Loops: Stress-Testing Policy Pinning, Budget Bounds, and Crash Recovery [7.414694666903067]
  GuardrailLoop is a simulation-based testbed that makes three operational contracts jointly testable.<n>A hash-pinned policy fixes goals, scope, evaluation identity, budget, and release conditions.<n>It separates useful adaptation, state recovery, and repeated execution.
  arXiv  Detail & Related papers  (2026-09-10T21:22:08Z)
• Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures [4.287745257410248]
  We present a runtime-assurance benchmark for cost-aware commit gates.<n>Its 48 task templates yield 2,880 scenarios across six fault regimes.<n>Results motivate explicit evidence lineage, cost-aware selection, and commit-time enforcement.
  arXiv  Detail & Related papers  (2026-09-10T01:42:40Z)
• Retry Amplification in Distributed Systems: A Systematic Analysis of Retry Policies and Their Role in Cascading Failures [0.0]
  This paper introduces the retry amplification factor (RAF), a metric quantifying the additional request volume that retry policies generate during partial failures.<n>In a study of 200 open-source Python microservice projects, explicit retry logic is detected in 11.5%.<n>We show that budget-constrained retries maintain success rates close to the no-retry baseline while still recovering from transient faults.
  arXiv  Detail & Related papers  (2026-08-26T06:04:03Z)
• Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair [36.56438114281786]
  We study the gap between finding a correct patch and retaining, verifying, and submitting it.<n>We derive an evidence-bound typed loop contract and instantiate its mechanically enforceable subset in a reference implementation.
  arXiv  Detail & Related papers  (2026-07-27T16:05:23Z)
• Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation [0.0]
  We document a failure class in frontier large language models.<n>The failure reproduces at first observation, but its empirical surface is unstable.<n>For regulated, frontier-model accuracy is a moving compliance boundary that shifts without notice.
  arXiv  Detail & Related papers  (2026-07-25T22:40:02Z)
• Fantastic Adaptive Taxonomies and How to Use Them [100.20614173157708]
  AdaMAST converts execution traces into a compact, evidence-grounded failure taxonomy.<n>No code is hand-authored, no trace human-annotated.<n>In agent-system search, taxonomy-coded diagnoses of failed candidates outperform free-form.
  arXiv  Detail & Related papers  (2026-07-17T17:41:16Z)
• SNARE: Adaptive Scenario Synthesis for Eliciting Overeager Behavior in Coding Agents [40.270213696031625]
  A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes.<n>We call this overeager behavior: the prompt is not adversarial and the run succeeds, yet an out-of-scope step can leak credentials or delete files.<n>Existing benchmarks miss it: task-completion suites credit any finished run, jailbreak suites probe adversarial prompts, and the one prior overeager benchmark applies a single fixed prompt to every agent-model pair.<n>We present SNARE, a pipeline that composes benign scenarios from reusable scope
  arXiv  Detail & Related papers  (2026-05-27T08:14:07Z)
• When Do LLM Agents Treat Surface Noise Differently from Semantic Noise? A 68-Cell Measurement Study with a Held-Out Trace-Level Validation [9.055086193088083]
  We document an empirical phenomenon in chain-of-thought and ReAct agents driven by ten large language models.<n>Mean-bearing perturbations alter final answers more often than presentation perturbations of comparable severity.
  arXiv  Detail & Related papers  (2026-05-25T15:57:11Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Correction and Corruption: A Two-Rate View of Error Flow in LLM Protocols [51.56484100374058]
  We propose a paired-outcome measurement interface for auditing a single protocol step on exact-match tasks.<n>For each instance, the interface records a baseline correctness bit and a post-step correctness bit.<n>These rates predict accuracy changes and define a reusable empirical interface testable across seeds, mixtures, and pipelines.
  arXiv  Detail & Related papers  (2026-04-20T13:25:40Z)
• SBEST: Spectrum-Based Fault Localization Without Fault-Triggering Tests [17.90798133817018]
  This study investigates the feasibility of using stack traces from crash reports as proxies for fault-triggering tests in Spectrum-Based Fault localization.<n>We propose SBEST, a novel approach that integrates stack trace information with test coverage data to perform fault localization when fault-triggering tests are missing.
  arXiv  Detail & Related papers  (2024-05-01T15:15:52Z)
• Regression with Multi-Expert Deferral [30.389055604165222]
  Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. We present a novel framework of regression with deferral, which involves deferring the prediction to multiple experts. We introduce new surrogate loss functions for both scenarios and prove that they are supported by $H$-consistency bounds.
  arXiv  Detail & Related papers  (2024-03-28T15:26:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.