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arxiv: 2605.01264 · v1 · submitted 2026-05-02 · 💻 cs.SE · cs.LG

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

Kushal Jasti , Tejamani Prashanth Sahu , Rishitha Pentyala , Muvvala Mohit , Vivek Yelleti This is my paper

Pith reviewed 2026-05-09 15:01 UTC · model grok-4.3

classification 💻 cs.SE cs.LG
keywords test case generationLLMmulti-agentcode coveragesoftware testingprompt evolutionfeedback agentslanguage agnostic
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The pith

FeedbackLLM uses two specialized LLM agents to extract coverage metadata and evolve prompts iteratively, producing test cases with higher line and branch coverage than baseline tools.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents FeedbackLLM as a language-agnostic framework for automated test case generation that addresses limitations of single-shot LLM approaches like hallucinations and redundancy. It operates in two stages: parsing source code to generate initial test cases based on input constraints, then applying Line Feedback and Branch Feedback agents to identify missed executions and unexecuted conditions. These agents communicate in tandem over multiple steps to refine the prompts, supported by a redundancy prevention cache. Evaluations on C and Python benchmarks show improved coverage metrics alongside linear scaling in execution time. A sympathetic reader would care because it offers a scalable alternative to manual or computationally heavy testing methods for complex software.

Core claim

FeedbackLLM demonstrates that a tightly coupled two-stage multi-agentic system, where specialized Line and Branch Feedback Agents extract metadata on missed lines and branches to evolve prompts over k iterations, can generate test cases achieving greater line and branch coverage than existing tools on standard C and Python benchmark programs while maintaining linear execution time scaling.

What carries the argument

The Line Feedback Agent and Branch Feedback Agent, which operate in tandem to communicate coverage metadata and refine evolving prompts in a repeated two-stage process.

Load-bearing premise

The Line Feedback Agent and Branch Feedback Agent can reliably extract accurate metadata about missed line executions and unexecuted branch conditions from test runs without significant hallucinations or errors.

What would settle it

Running the system on a benchmark where the feedback agents consistently misreport coverage data, resulting in no increase or a decrease in achieved coverage after multiple iterations compared to the initial generation.

Figures

Figures reproduced from arXiv: 2605.01264 by Kushal Jasti, Muvvala Mohit, Rishitha Pentyala, Tejamani Prashanth Sahu, Vivek Yelleti.

Figure 1
Figure 1. Figure 1: Systematic representation of the KSLLM automated test case view at source ↗
Figure 2
Figure 2. Figure 2: Schematic diagram of the proposed approach view at source ↗
Figure 3
Figure 3. Figure 3: Branch Coverage yielded by kS-LLM and FeedbackLLM (C) view at source ↗
Figure 4
Figure 4. Figure 4: Line Coverage yielded by kS-LLM and FeedbackLLM (C) view at source ↗
Figure 5
Figure 5. Figure 5: Branch Coverage yielded by FeedbackLLM (C) and FeedbackLLM view at source ↗
Figure 6
Figure 6. Figure 6: Line Coverage yielded by FeedbackLLM (C) and FeedbackLLM (Py) view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript proposes FeedbackLLM, a language-agnostic automated test case generation framework using a two-stage multi-agent LLM architecture. The first stage parses source code to extract input constraints and generate initial test cases; the second stage employs a Line Feedback Agent (to extract metadata on missed line executions) and a Branch Feedback Agent (to extract metadata on unexecuted branch conditions) that operate iteratively in tandem over k steps to evolve prompts. A redundancy prevention cache is added to reduce duplicate API calls. Evaluation is performed on standard C and Python benchmark programs, with the central claim that the approach achieves higher line and branch coverage than baseline tools while exhibiting linear scaling in execution time.

Significance. If the empirical results hold with proper validation, the work could advance LLM-based software testing by demonstrating how structured, metadata-driven feedback loops can mitigate single-shot prompting issues such as hallucinations and redundancy. The language-agnostic design and explicit redundancy cache are practical strengths that address scalability concerns in complex systems.

major comments (2)
  1. [Abstract] Abstract: the claim that FeedbackLLM 'demonstrated more line and branch coverage than baseline tools while scaling linearly in execution time' is presented without any quantitative coverage percentages, execution-time measurements, baseline tool names, statistical significance tests, or error bars. This absence directly undermines verification of the central performance claim.
  2. [Feedback agents description] Description of the feedback agents (Section 3 or equivalent): the two-stage process depends on the Line Feedback Agent and Branch Feedback Agent reliably extracting accurate metadata without introducing hallucinations or extraction errors. No accuracy validation, manual inspection results, or error analysis for these agents is supplied, which is load-bearing for the iterative improvement mechanism.
minor comments (3)
  1. The phrase 'standard benchmark programs' is used without naming the specific suites or providing citations, making reproducibility difficult.
  2. The iteration count k is referenced but neither its typical value nor the stopping criterion is specified.
  3. Implementation details of the redundancy cache (e.g., key structure, eviction policy) and its measured impact on API calls are missing.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of clarity and validation that we will address in the revision. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that FeedbackLLM 'demonstrated more line and branch coverage than baseline tools while scaling linearly in execution time' is presented without any quantitative coverage percentages, execution-time measurements, baseline tool names, statistical significance tests, or error bars. This absence directly undermines verification of the central performance claim.

    Authors: We agree that the abstract would benefit from greater specificity to allow immediate verification of the claims. The experimental evaluation section already reports concrete line/branch coverage values, execution times across the C and Python benchmarks, the specific baseline tools compared, and observations on linear scaling. In the revised version we will condense the key quantitative results (e.g., average coverage deltas and time measurements) into the abstract while retaining the high-level claim, thereby making the performance summary self-contained without altering the underlying data. revision: yes

  2. Referee: [Feedback agents description] Description of the feedback agents (Section 3 or equivalent): the two-stage process depends on the Line Feedback Agent and Branch Feedback Agent reliably extracting accurate metadata without introducing hallucinations or extraction errors. No accuracy validation, manual inspection results, or error analysis for these agents is supplied, which is load-bearing for the iterative improvement mechanism.

    Authors: The referee correctly identifies that the agents' metadata extraction accuracy is central to the iterative loop. While the manuscript demonstrates end-to-end coverage gains and the role of the redundancy cache in reducing duplicates, it does not contain a dedicated error analysis or manual validation of the agents' outputs. We will add this in the revision by including a targeted evaluation: manual inspection of a representative sample of agent-generated metadata, reported extraction accuracy rates, and discussion of how the tandem agent interaction and cache mitigate hallucinations. This analysis will be performed on the existing benchmark runs. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical engineering contribution describing a multi-agent LLM framework for automated test case generation. It reports benchmark results on C and Python programs for line/branch coverage and runtime scaling, with no mathematical derivations, equations, fitted parameters, or self-referential definitions. The central claims rest on experimental outcomes from the described architecture rather than any reduction to inputs by construction or self-citation chains. No load-bearing steps match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The framework rests on domain assumptions about LLM parsing and feedback utility plus two newly introduced agent entities; no free parameters are quantified in the abstract.

axioms (2)
  • domain assumption LLMs can parse source code to extract input constraints and generate initial test cases
    Invoked in the first stage of the two-stage approach described in the abstract.
  • domain assumption Coverage metadata from execution can be used by LLMs to evolve prompts and improve subsequent test cases
    Core premise of the iterative k-step feedback loop between the two agents.
invented entities (2)
  • Line Feedback Agent no independent evidence
    purpose: Extracts metadata related to missed line executions to guide prompt evolution
    New specialized agent role introduced in the second stage; no independent evidence or prior citation provided in abstract.
  • Branch Feedback Agent no independent evidence
    purpose: Extracts metadata of unexecuted branch conditions to guide prompt evolution
    New specialized agent role introduced in the second stage; no independent evidence or prior citation provided in abstract.

pith-pipeline@v0.9.0 · 5571 in / 1584 out tokens · 50492 ms · 2026-05-09T15:01:21.294414+00:00 · methodology

discussion (0)

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Reference graph

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