FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback

• URL: http://arxiv.org/abs/2605.01264v1
• Date: Sat, 02 May 2026 05:43:29 GMT
• Title: FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback
• Abstract summary: Traditional approaches to test case generation often involve manual effort and incur significant computational overhead.<n>We propose FeedbackLLM, a novel automated language-agnostic test case generation framework based on tightly coupled two-stage approach.<n>The performance of the proposed architecture is evaluated on the standard benchmark programs related to C and Python programs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional approaches to test case generation often involve manual effort and incur significant computational overhead. Additionally, these approaches are not scalable, and hence, unsuitable for complex software systems. Recently, Large Language Models (LLMs) have been applied to software testing. However, single-shot prompt engineering-based approaches tend to hallucinate and generate redundant test cases, resulting in fewer branches. To handle the above-mentioned limitations, in this paper, we propose FeedbackLLM, a novel automated language-agnostic test case generation framework based on tightly coupled two-stage approach. In the first stage, FeedbackLLM extracts the input constraints by parsing source code and generates the possible test cases. The quality of the test cases is evaluated in the second stage by the following two specialized LLM feedback agents: (i) Line Feedback Agent: extracts the metadata related to missed line executions and (ii) Branch Feedback Agent: extracts the metadata of the unexecuted branch conditions. The above agents operate in a two-stage process, communicating in tandem, and this procedure is repeated for k-steps. Further, we also introduced a redundancy prevention cache to avoid duplicate API requests and avoid unnecessary execution cycles. The performance of the proposed architecture is evaluated on the standard benchmark programs related to C and Python programs. FeedbackLLM demonstrated more line and branch coverage than baseline tools while scaling linearly in execution time.

Related papers

• TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference [47.62941623025392]
  We present TELLER, a non-intrusive and Log-aware LLM inference Root-cause analysis framework.<n>TELLER collects NVTX/CUPTI traces and service logs without modifying model binaries.<n>It reconstructs per-request call-chain trees and aligns log lines with corresponding execution steps.<n>On top of these representations, TELLER combines numeric candidate localization with a multimodal root-cause model.
  arXiv  Detail & Related papers  (2026-08-03T09:39:11Z)
• MuMuTestUp: Mutation-based Multi-Agent Test Case Update [8.951495370176216]
  We propose MuMuTestUp, a mutation-guided multi-agent framework with three specialized agents.<n>MuMuTestUp generates repair instructions for uncovered lines/branches, handles hallucinations via semantic-similarity search.<n>We also construct PRBENCH, a 571-sample pull-request-level dataset from 10 open-source Java projects.
  arXiv  Detail & Related papers  (2026-05-19T02:25:25Z)
• From I/O to Code with Discovery Agent [103.88427301265669]
  We propose DIO-Agent, a discovery agent for IO2Code.<n>Our method frames IO2Code as an evolutionary search over discrete program space.<n>Extensive experiments show that DIO-Agent consistently outperforms both traditional program-by-example method and SOTA evolution-agent baselines.
  arXiv  Detail & Related papers  (2026-05-14T18:57:32Z)
• LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
  Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
  arXiv  Detail & Related papers  (2026-05-02T11:31:33Z)
• An Agentic Evaluation Framework for AI-Generated Scientific Code in PETSc [7.236134946837382]
  petscagent-bench is an agentic framework built on an agents-evaluating-agents paradigm.<n>It orchestrates a 14-evaluator pipeline across five scoring categories: correctness, performance, code quality, algorithmic appropriateness, and library-specific conventions.<n>We demonstrate the framework on a benchmark suite of realistic problems using the PETSc library for HPC.
  arXiv  Detail & Related papers  (2026-03-16T22:46:10Z)
• AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents [50.481033105867205]
  We introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories.<n>The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement.<n>It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity.
  arXiv  Detail & Related papers  (2026-03-15T16:13:58Z)
• Enhancing LLM-Based Test Generation by Eliminating Covered Code [2.2566909388480743]
  Large Language Models (LLMs) have shown promise in improving test generation.<n>We propose a scalable LLM-based unit test generation method.<n>Our approach outperforms state-of-the-art LLM-based and search-based methods.
  arXiv  Detail & Related papers  (2026-02-25T15:16:43Z)
• Scaling Agentic Verifier for Competitive Coding [66.11758166379092]
  Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt.<n>Execution-based re-ranking offers a promising test-time scaling strategy, yet existing methods are constrained by either difficult test case generation or inefficient random input sampling.<n>We propose Agentic Verifier, an execution-based agent that actively reasons about program behaviors and searches for highly discriminative test inputs.
  arXiv  Detail & Related papers  (2026-02-04T06:30:40Z)
• A Benchmark for Procedural Memory Retrieval in Language Agents [0.023227405857540805]
  Current AI agents excel in familiar settings, but fail sharply when faced with novel tasks with unseenProc.<n>We present the first benchmark that isolates procedural memory retrieval from task execution.<n>Our results expose a clear generalization cliff: embedding-based methods perform strongly on familiar contexts, yet degrade considerably on novel ones.
  arXiv  Detail & Related papers  (2025-11-21T08:08:53Z)
• Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking [54.43083499412643]
  Test-time algorithms that combine the generative power of language models with process verifiers offer a promising lever for eliciting new reasoning capabilities.<n>We introduce a new process-guided test-time sampling algorithm, VGB, which uses theoretically grounded backtracking to achieve provably better robustness to verifier errors.
  arXiv  Detail & Related papers  (2025-10-03T16:21:14Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• TestWeaver: Execution-aware, Feedback-driven Regression Testing Generation with Large Language Models [5.871736617580623]
  Regression testing ensures that code changes do not unintentionally break existing functionality.<n>Recent advances in large language models (LLMs) have shown promise in automating test generation for regression testing.<n>We present TestWeaver, a novel approach that integrates lightweight program analysis to guide test generation more effectively.
  arXiv  Detail & Related papers  (2025-08-02T08:13:02Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• Tractable Offline Learning of Regular Decision Processes [50.11277112628193]
  This work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs) Ins, the unknown dependency of future observations and rewards from the past interactions can be captured experimentally. Many algorithms first reconstruct this unknown dependency using automata learning techniques.
  arXiv  Detail & Related papers  (2024-09-04T14:26:58Z)
• Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM [32.44432906540792]
  We present SymPrompt, a code-aware prompting strategy for large language models in test generation. SymPrompt enhances correct test generations by a factor of 5 and bolsters relative coverage by 26% for CodeGen2. Notably, when applied to GPT-4, SymPrompt improves coverage by over 2x compared to baseline prompting strategies.
  arXiv  Detail & Related papers  (2024-01-31T18:21:49Z)

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.