An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering

Dr Tom Osborn; Gulam Husain; Roshankumar Bisoi; Waliur Rahman; Zaber Al Hassan Ayon

REVIEW 4 major objections 5 minor 1 cited by

An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A scraped DOM, reformatted as a typed page hierarchy with navigation flow, lets a large language model generate executable tests with 90% and 70% success on two applications.

desk verdict A concrete encoding for web app structure, but the reported success rates don't get causal because no one ran the counterfactual. read the letter →

arxiv 2501.06837 v1 pith:ICN3KVGG submitted 2025-01-12 cs.AI cs.SE

Zaber Al Hassan Ayon , Gulam Husain , Roshankumar Bisoi , Waliur Rahman , Dr Tom Osborn This is my paper · ORCID

classification cs.AIcs.SE

keywords LargeLanguageModelIn-ContextLearningDocumentObjectGenerativeAIHierarchicalRepresentationEnterpriseTestAutomationIntelligentQualityEngineering

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a web application can be summarized into a hierarchical text representation—page types, section contexts, element details with unique identifiers, and navigation flow—that lets a large language model understand the whole site within a prompt. The authors build a five-phase pipeline that scrapes the DOM, synthesizes the site structure, generates Selenium test cases from instructions, executes them, and summarizes results. On a login flow and a signup flow, they report 90% and 70% execution success rates and 'High' relevance across evaluation criteria. A sympathetic reader would take the claim to be that this representation, not the LLM's general coding skill alone, is what makes generated tests executable and relevant. If true, the approach offers a path toward autonomous end-to-end quality engineering for enterprise web applications without model fine-tuning.

What carries the argument

The central object is the hierarchical site structure produced in the multi-page analysis phase: page-level summaries, section-level contexts, element-level details with unique IDs, and a navigation flow with priorities. A closed set of seven page types (login, signup, account, listing, detail, form, static) anchors the representation, while unique element identifiers prevent confusion between sibling elements whose locators look similar. The mechanism is that this structure fits within the LLM's context window page-wise while preserving cross-page relationships, so few-shot in-context learning can generate tests that follow both instructions and application flow.

What would settle it

Run the identical prompt set and Selenium harness on both applications under four input conditions: the proposed hierarchy, raw DOM, naive page-wise chunking, and a flat element list without page types or navigation flow. If execution success and relevance ratings do not fall in the non-hierarchical conditions, the observed 90% and 70% cannot be attributed to the representation.

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Extended reading notes

Core claim

The paper claims that the bottleneck in using LLMs for web testing is not the model but how the application is presented. Its discovery is a representation that keeps the DOM's hierarchy intact: each element gets a unique identifier alongside its locator, pages are typed from a closed set (login, signup, account, listing, detail, form, static), elements are grouped into logical sections, and navigation flow and priority are encoded. Fed this structure page by page, an LLM produces test cases that are validated for structural integrity, uniqueness, and relevance, mapped to real or synthetic test data, and translated into Selenium actions. On ten login tests for an e-commerce app and ten signup tests for a healthcare app, 90% and 70% executed successfully, with relevance rated 'High' on instruction adherence, application fit, data mapping, and synthetic data contextuality. The authors also report that the LLM's correctness on their priority-prediction task was approximately 87%.

Load-bearing premise

The claim attributes the test success to the hierarchical representation, but the experiments never compare it with the same LLM and execution harness using raw DOM, naive chunking, or flat element lists; on such simple flows those alternatives might perform equally well.

Editorial extensions

If this is right

Login and signup test suites can be generated and executed largely automatically: nine of ten login tests and seven of ten signup tests passed on the first pipeline run.
The same representation drives test case generation, test data mapping, and result reporting, so generated suites and failure summaries stay tied to the actual page structure.
Page-wise DOM chunking with navigation flow avoids feeding the whole DOM, keeping inputs within the LLM context window while retaining site-level understanding.
The authors estimate time savings above 50% versus traditional automation once setup is complete, with the savings more pronounced in maintenance as the application scales.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the representation is the causal factor, then ablating it—raw DOM, naive chunking, or a flat element list—should drop execution success and instruction relevance; this is a direct testable consequence the paper does not report.
The closed set of seven page types may not cover flows like checkout wizards or dashboards; a natural extension is to expand or learn page types and measure whether success holds.
Because the method passes page-wise hierarchical chunks instead of the whole DOM, its gains could come partly from context-window management; separating that from hierarchical ordering would clarify the mechanism.
If the reported time savings generalize, the representation may be more valuable for keeping tests alive across application changes than for first-generation test creation, since the experiments do not directly test maintenance.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing you should know: the headline success rates (90%, 70%) don't actually support the paper's causal claim that the hierarchical representation improves LLM test generation. There is no baseline, no ablation, and the prompts in 5.1.2 already tell the LLM which tests to write. The representation's contribution is unmeasured.

What is genuinely new: the exact encoding—unique element IDs, a closed page-type set (login, signup, account, listing, detail, form, static), page-wise DOM chunking, and navigation-flow metadata—is a concrete variant not in the cited literature. The paper also gives a sensible pipeline: scrape DOM elements, build page-level summaries, generate test cases, execute via Selenium, and report with LLM summaries. The execution results are real (20 tests, 16 passed), which shows feasibility: the encoding does not break the LLM. That is a legitimate practical data point.

Soft spots, in order. The big one is the missing counterfactual: keeping the prompts and harness fixed while varying only the input representation would be necessary to support 'significantly enhances.' Instead, Section 4.2.1 gives anecdotal complaints about full-DOM and chunking approaches without numbers. Second, 10 tests per app with no error bars is small, and the relevance ratings in Table 4 are self-defined and qualitative. Third, the '87% correct' and '50% time savings' are stated without methodology. Fourth, no artifacts are released, so independent verification is impossible. These are load-bearing gaps for any comparative claim.

That said, I don't think the paper is incoherent or a scam. It's a plausible engineering report with a concrete representation that might be useful to practitioners building LLM-based test automation. The clear thinking is there—they identified real problems (sibling locators, context limits) and made reasonable choices. The writing is rough in places (typos, vague phrases), but the structure is understandable.

Who this is for: a practitioner who wants a starting point for representing web apps to an LLM, not a researcher needing validated evidence. I'd bring it to a reading group as an example of how not to evaluate a technique, but I wouldn't cite it as evidence in my own work.

Recommendation: send to peer review anyway. The method is concrete and the feasibility data is real, and a decent reviewer could push the authors to add baselines and release artifacts. It's a borderline but not a desk-reject.

Referee Report

4 major / 5 minor

Summary. The paper proposes a five-phase pipeline for representing enterprise web applications as a hierarchical structure (page-level summaries, section contexts, element details, and navigation flow) to enable LLM-based in-context test generation, Selenium execution, and AI-generated reporting. The central claim is that this representation materially improves the quality and executability of generated tests, supported by reported 90% and 70% execution success rates on two applications (Swag Labs and MediBox), a claim of approximately 87% correctness for one intermediate LLM output, and an assertion of upwards of 50% time savings over traditional automation.

Significance. If the causal claim were established, the hierarchical representation would be a practical contribution to automated functional web testing, offering a structured alternative to raw DOM dumps or naive chunking. The paper's strengths are that it reports real Selenium executions against two actual applications, lists concrete failure modes (timeouts, NoneType errors, assertion failures), and describes an end-to-end pipeline rather than a single component. However, the experimental design does not isolate the representation's contribution, the sample is tiny (20 tests total), the relevance metrics are self-assessed qualitative labels, and no artifact or baseline is provided; as a result, the significance of the claimed effect is not currently supportable.

major comments (4)

[Section 5, Table 3 and Section 5.1.2] The central causal claim that the hierarchical representation 'significantly enhances' test generation is not supported because no baseline or ablation varies only the input representation while keeping prompts and execution harness fixed. The prompts reproduced in Section 5.1.2 already enumerate the exact scenarios to generate (e.g., 'Generate a detailed test plan for logging in... valid and invalid credentials' and 'Create and execute a minimum of 10 functional test scripts specifically for the user signup process'), so the observed 90% and 70% success rates could plausibly be driven by prompt specificity, the simplicity of the login and signup flows, the Selenium harness, or the LLM's general coding ability. The statement in Section 4.2.1 that full DOM and chunking approaches 'induced problems' is anecdotal and unquantified, so it cannot establish the representation's contribution.
[Section 3.3] The claim that 'the information received from LLM after this operation to be correct approximately 87% of the time' is given without any definition of correctness, sample size, evaluation procedure, or error bars. This number is not tied to any table or experiment in the paper and cannot be assessed; it should either be substantiated with a defined measurement or removed.
[Section 5, Table 4 and Section 4.7] The relevance criteria ('Instruction Relevance', 'Web Application Relevance', 'Data Mappings Relevance', 'Synthetic Data Contextuality') are reported only as qualitative 'High' labels with no rubric, independent raters, or inter-rater agreement. Combined with only 20 executed test cases and no release of prompts, code, or logs, the evaluation is insufficient to justify the broad conclusion that the representation approach significantly improves LLM-based test generation. A quantitative scoring scheme and a larger, more diverse set of applications or tasks would be needed.
[Section 3.2, item 4] The closed set PT = {login, signup, account, listing, detail, form, static} is asserted to cover 'any page from any web application', but no evidence is given for its completeness or for how the page-type labels were validated. Because page-type identification is a component of the representation, this unsupported universal claim weakens the paper's generalization argument; the authors should at least explain how the set was derived and test it against a broader corpus of pages.

minor comments (5)

[Section 3] The methodology section states it is 'divided into four phases', but Sections 3.1 through 3.5 describe five phases; please correct the count or renumber consistently.
[Section 1 and Section 3.2] There are typos in the text: 'fulfilling thee processes' in Section 1 and 'out test generation approach' in Section 3.2 should read 'the' and 'our', respectively.
[Section 2] The reference to 'Jen-tse et al.' is written as 'Jen-Tse et al. Jen-tse et al.' and appears inconsistently; please unify the citation format.
[Section 5, Table 4] Table 4 mixes quantitative success rates (90% and 70%) with qualitative labels ('High') in the same 'Success Rate' columns; consider separating these or renaming the columns to avoid conflation.
[Section 5] The platform name 'Flame' is introduced without any description; please define it at first use or remove the reference.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central test-execution results are externally grounded in Selenium pass/fail outcomes, and no load-bearing claim reduces to its inputs by definition or self-citation.

full rationale

The paper's central quantitative claims are the 90% and 70% Selenium execution success rates (Section 5.1.1, Table 3), which are empirical pass/fail outcomes of generated scripts executed against real web applications. The hierarchical representation is not defined in terms of these outcomes, and no fitted parameter is renamed as a prediction. The only potentially self-referential evaluation criterion is 'relevance of the test cases based on instruction provided' (Section 5.1.2), where test cases are compared against instructions that were also included in the LLM prompt; this is a weak, partially tautological metric, but it is not the load-bearing result and does not make the overall derivation circular. The paper contains no load-bearing self-citations, imports no uniqueness theorem from prior author work, and does not smuggle an ansatz in via citation. The main validity threat is that no baseline or ablation isolates the representation's causal contribution, so the claim that the representation 'significantly enhances' LLM test generation is under-supported; however, that is an experimental-design and correctness concern, not circularity of the kind where a result equals its input by construction. Accordingly, the appropriate circularity finding is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no free parameters or fitted constants. Its claims rest on domain assumptions about page types, chunking, unique IDs, and the adequacy of in-context learning; these are plausible but not independently verified. No new theoretical entities are introduced.

assumptions (4)

ad hoc to paper For any page from any web application, the page type is a member of the closed set PT = {login, signup, account, listing, detail, form, static}.
Introduced in Section 3.2, Phase 2 item 4. The universal quantification is stated without empirical validation on diverse applications, yet later test generation relies on these types.
domain assumption Page-wise DOM chunking plus navigation-flow metadata preserves enough hierarchical context for LLM test generation.
Section 4.2.1 claims traditional chunking failed and this approach works, but no controlled comparison or quantitative evidence is provided.
domain assumption Assigning unique element identifiers resolves locator ambiguity that misleads LLMs.
Section 4.2.1 presents this as an experimental observation, but no measurements of misidentification rates with and without unique IDs are given.
domain assumption LLM in-context learning is the best approach for dynamic enterprise test automation compared with fine-tuning.
Section 3 argues fine-tuning is costly and time-consuming; this is reasonable but unproven, and it is not the target result of the paper.

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Cite this review

Pith. "Pith review of An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering." pith.science (2026). https://pith.science/paper/ICN3KVGG

@misc{pith2026250106837,
  author       = {Pith},
  title        = {Pith review of: An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ICN3KVGG}},
  note         = {Machine review of arXiv:2501.06837}
}

read the original abstract

This paper presents a novel approach to represent enterprise web application structures using Large Language Models (LLMs) to enable intelligent quality engineering at scale. We introduce a hierarchical representation methodology that optimizes the few-shot learning capabilities of LLMs while preserving the complex relationships and interactions within web applications. The approach encompasses five key phases: comprehensive DOM analysis, multi-page synthesis, test suite generation, execution, and result analysis. Our methodology addresses existing challenges around usage of Generative AI techniques in automated software testing by developing a structured format that enables LLMs to understand web application architecture through in-context learning. We evaluated our approach using two distinct web applications: an e-commerce platform (Swag Labs) and a healthcare application (MediBox) which is deployed within Atalgo engineering environment. The results demonstrate success rates of 90\% and 70\%, respectively, in achieving automated testing, with high relevance scores for test cases across multiple evaluation criteria. The findings suggest that our representation approach significantly enhances LLMs' ability to generate contextually relevant test cases and provide better quality assurance overall, while reducing the time and effort required for testing.

Figures

Figures reproduced from arXiv: 2501.06837 by the authors.

**Figure 1.** Overall phase flow 2. Validation of hierarchical representation capability of Document Object Model (DOM) structure and interactive behaviour of web applications. 3. To assess the automated test case generation process using LLMs 4. Assess and validate automated test case generation process using LLMs, focusing on functional validation of web application features 5. Analyse the effectiveness of few-shot learning in … view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions
cs.SE 2025-06 conditional novelty 4.0 of 10

AI models can generate and execute test cases on a demo app, but the reported flakiness rate masks a more serious false-negative problem.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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Coral Calero. Quality in web engineering. In 2010 Seventh International Conference on the Quality of Information and Communications Technology. IEEE, 2010

work page 2010

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Applications of automated model’s extraction in enterprise systems

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An empirical study to compare three web test automation approaches: NLP-based, programmable, and capture&replay

Maurizio Leotta, Filippo Ricca, Alessandro Marchetto, and Dario Olianas. An empirical study to compare three web test automation approaches: NLP-based, programmable, and capture&replay. Journal of Software: Evolution and Process, 36(5), 2024

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