From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks

Adeesha Wijayasiri; Danuka Lakshan; Himindu Kularathne; Methmini Madhushika; Mineth Weerasinghe; Nisansa de Silva; Srinath Perera

REVIEW 5 major objections 6 minor 1 cited by

From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks

T0 review · 5 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read A unified comparison of nine microservice decomposition tools on four Java monoliths finds that hierarchical density clustering (HDBScan) produces the most consistently balanced service boundaries.

desk verdict The paper's headline recommendation—HDBScan as most balanced—rests on imported outlier metric values, so the ranking is a useful hypothesis rather than a measurement; the new local runs and honest limitations section are the real value. read the letter →

arxiv 2601.23141 v1 pith:KZ35QXIG submitted 2026-01-30 cs.SE

Mineth Weerasinghe , Himindu Kularathne , Methmini Madhushika , Danuka Lakshan , Nisansa de Silva , Adeesha Wijayasiri , Srinath Perera This is my paper · ORCID

classification cs.SE

keywords microservicedecompositionserviceboundaryidentificationHDBScanhierarchicalclusteringstructuralmodularitybenchmarkcomparisonmonolith-to-microservices

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 attempts to settle which automated decomposition tool should be used when breaking monolithic Java applications into microservices. It brings together nine tools spanning static, dynamic, and hybrid approaches, evaluates them on four benchmark monoliths, and scores every decomposition with one composite metric. The central claim is that HDBScan, a hierarchical density-based clustering method, consistently produces the most balanced decompositions—strong structural modularity with low interface counts and low inter-service communication. A practitioner reading this would come away with a specific first choice for medium-size Java monoliths, and a reproducible pipeline for future tool comparisons.

What carries the argument

The ranking mechanism is a two-part apparatus. First, HDBScan is a hierarchical variant of DBSCAN that forms service clusters from class dependency graphs without requiring the number of services in advance. Second, the paper's comparison pipeline computes four quality metrics for every decomposition and converts them into a single score via z-score normalization per benchmark, weighted W={3,−1,−1,−1} to reward structural modularity and penalize interface number, inter-partition communication, and non-extreme distribution. The z-scoring is what lets a method that is merely average on SM but outstandingly low on IFN/ICP, like HDBScan, top the ranking.

What would settle it

Run HDBScan on JPetStore, AcmeAir, DayTrader, and Plants using the same metric pipeline the paper applied to CHGNN and MonoEmbed, and compare its recomputed IFN/ICP with the quoted values. If HDBScan's composite score falls below a-BMSC or MEM, the central conclusion is refuted.

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Extended reading notes

Core claim

Across all four benchmark systems—JPetStore, AcmeAir, DayTrader, and Plants—HDBScan earns the highest or near-highest aggregate score, and the paper concludes that 'hierarchical clustering-based methods, particularly HDBScan, produce the most consistently balanced decompositions across benchmarks.' The score is computed by z-normalizing four metrics (Structural Modularity, Interface Number, Inter-partition Communication, Non-Extreme Distribution) within each benchmark and combining them with weights of +3 for SM and −1 for the other three. HDBScan's edge comes largely from very low IFN and ICP values, which offset its moderate modularity and NED, while fine-grained a-BMSC and coarse-grained

Load-bearing premise

The paper treats metric values taken from other papers as directly comparable to values it recomputed itself, even though implementations, configurations, and dataset versions differ—especially for HDBScan's extreme IFN and ICP cells, which were not independently recomputed.

Editorial extensions

If this is right

Practitioners decomposing medium-size Java monoliths can treat HDBScan-style hierarchical density clustering as a strong default, since it balances cohesion, coupling, interface simplicity, and size distribution.
Fine-grained tools like a-BMSC produce more microservices and pay a coordination penalty; coarse-grained tools like Mono2Micro may yield poor size balance and higher interface counts on some systems.
Embedding-based (MonoEmbed) and graph-neural (CHGNN) methods show high dataset sensitivity and should be tuned per system rather than used out of the box.
The unified metric pipeline and composite score give future tool evaluations a reproducible template for the four benchmarks.

Reading between the lines

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

The most fragile number in the table is HDBScan's IFN/ICP, which were quoted from its original paper rather than recomputed; rerunning HDBScan under the paper's own pipeline is the direct test of the first-place conclusion.
The composite weights are manually chosen; changing the relative penalty of IFN vs NED, for example, could shuffle the ranking, so the 'balanced' conclusion is partly built into the aggregation rule.
Because ground-truth datasets such as TrainTicket were excluded, the comparison captures structural and communication proxies, not semantic correctness of service boundaries; a ground-truth evaluation could overturn the ranking.
The evidence covers only four systems, all medium-size Java monoliths; extending to larger codebases or other languages would test whether HDBScan's advantage generalizes.

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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

Here's the deal. The paper is a comparative evaluation of microservice decomposition tools. The genuinely new part is small but real: the authors ran CHGNN and MonoEmbed on four standard benchmarks (JPetStore, AcmeAir, DayTrader, Plants) and computed SM, IFN, ICP, NED through their own pipeline. For every other tool—Bunch, MEM, FoSCI, CoGCN, Mono2Micro, HDBScan, a-BMSC—the metric values are taken from published papers, mostly [18] and [21]. They then z-score all values for each benchmark and combine them into a weighted score using weights borrowed from Sellami and Saied [20], the paper that introduced MonoEmbed.

The writing is clear and the authors are unusually honest: Section 5 concedes that mixing reported and recomputed values "may affect comparability due to differences in implementations, configurations and dataset versions, particularly for ICP and IFN," and it lists the absence of statistical testing, variance reporting, sensitivity analysis, and ground-truth validation. That candor earns credit.

The problem is that the central claim does not survive the evidence. HDBScan's top score is driven almost entirely by IFN and ICP values imported from [21] that are extreme outliers (e.g., IFN 0.05–0.30, ICP 0.02–0.05 across benchmarks) relative to every tool run under the paper's own metric definitions. If those values came from a different counting rule—REST endpoints vs. method calls, or dynamic traces vs. static call graphs—the z-score normalization is invalid and the ranking collapses. The paper's own limitations section effectively says that. Additionally, HDBScan's NED values (0.70–0.90) are among the worst on two benchmarks, which undercuts the abstract's phrase "balanced decompositions." The aggregate weight vector is also endogenous: it comes from the MonoEmbed paper, and the metric definitions mostly from other evaluated tools' papers.

So what's the honest verdict? This is a useful, well-organized survey with a small amount of new experimental data and a transparent if fragile aggregation scaffold. It is not a robust empirical ranking. The headline conclusion should be reframed as a hypothesis—"density-based hierarchical clustering appears promising, pending a common-pipeline comparison"—or the authors should run all nine tools in one pipeline with variance and sensitivity analysis. Given the limitations the authors themselves acknowledge, I would treat the ranking as indicative, not definitive.

Who is this for? Researchers and practitioners working on microservice decomposition who want a bird's-eye view of the field and a few new data points. It deserves a serious referee—there is real content here—but the referee should push hard for a re-run of all tools or a much more cautious conclusion. I'd accept for peer review with the expectation of major revision.

Referee Report

5 major / 6 minor

Summary. This paper presents a comparative evaluation of nine microservice decomposition tools (Bunch, MEM, FoSCI, CoGCN, Mono2Micro, HDBScan, a-BMSC, CHGNN, MonoEmbed) on four Java benchmark systems (JPetStore, AcmeAir, DayTrader, Plants). The authors recompute results for CHGNN and MonoEmbed through a 'unified metric computation pipeline' and combine these with metric values imported from prior publications. They compute an aggregate score by z-scoring each metric per benchmark and applying a fixed weight vector W={3,-1,-1,-1}, and on this basis conclude that HDBScan produces the most consistently balanced decompositions.

Significance. If the comparison were fully credible, the paper would be a useful consolidation for practitioners choosing a decomposition technique: it brings nine tools onto four common benchmarks and makes an explicit, falsifiable ranking claim. The study also has strengths: it identifies a real problem in the field (incompatible evaluation setups), it contributes locally reproduced results for two tools, and its Limitations section is unusually candid about threats to validity. However, the headline conclusion is not supported by the evidence as presented: the decisive HDBScan values are imported from another paper and are extreme outliers on the very metrics that drive the aggregate score, while the paper itself concedes that mixing reported and recomputed values may break comparability. The paper does not ship code or data, so the reproducibility promised in the abstract is only partial.

major comments (5)

[§3, §4, §5, Table 1] The central claim that HDBScan ranks first is load-bearing on imported cells. Section 3 states that for all tools except CHGNN and MonoEmbed, metric values were taken directly from prior papers, and Section 5 concedes that this 'may affect comparability due to differences in implementations, configurations and dataset versions, particularly for ICP and IFN.' HDBScan's IFN values (0.05, 1.00, 0.30, 0.30) and ICP values (0.05, 0.03, 0.02, 0.02) are one to two orders of magnitude lower than nearly every recomputed or reported cell from other tools. If [21] used different interface definitions or different call-graph/dynamic-trace semantics than Equations 2–3, the z-score normalization is invalid and the top ranking is an artifact. The paper must either recompute HDBScan under the same pipeline, provide a sensitivity analysis that re-ranks tools without the imported cells, or substantially w
[§4, Abstract, Table 1] The characterization of HDBScan as producing the 'most consistently balanced decompositions' is contradicted by its NED values: 0.70, 0.80, 0.90, 0.90 across the four benchmarks. These are among the worst NED values in the table (e.g., CoGCN on AcmeAir is 1.00, but HDBScan is worse than MEM, FoSCI, Mono2Micro, CHGNN, and MonoEmbed on several systems). The aggregate score rewards HDBScan despite its poor size balance because the imported low IFN/ICP values dominate the weighted sum. The abstract and Section 4 should be reworded to say that HDBScan achieves high aggregate scores driven by low interface and communication counts, while its service-size balance is actually a weakness.
[§3, Eq. (5)] The aggregate score's construction is not robust enough to carry the paper's ranking conclusions. First, the weight vector W={3,-1,-1,-1} is taken from [20], which is the paper introducing MonoEmbed, one of the evaluated tools; using the same authors' weighting to evaluate their own tool is an endogeneity risk that should at least be tested. Second, with z-scores computed over only 8–9 tools per benchmark, a single extreme imported cell can dominate the aggregate. Third, Section 5 itself notes 'formulation ambiguities in Equation 5 that may hinder exact reproducibility.' The authors should report per-metric rankings and a sensitivity analysis over alternative weight vectors, or explicitly frame the aggregate score as exploratory rather than as the basis for the headline claim.
[§4, §5, Table 1] The paper makes comparative claims such as 'HDBScan obtains high scores; often ranking first' and 'most consistently strong results,' but Section 5 admits there is no statistical testing, no variance reporting, and no sensitivity analysis. In Table 1, several aggregate scores are close (e.g., DayTrader: CoGCN 0.73 vs. HDBScan 0.47 vs. a-BMSC 0.49; AcmeAir: MEM 0.69 vs. HDBScan 0.53), so with single reported values the ranking could change under modest measurement error. The authors should either run repeated executions where feasible, provide confidence intervals, or downgrade the comparative conclusions to descriptive observations.
[Table 1] HDBScan's Micro (number of microservices) column is missing for all four benchmarks. Without the service count, the reported NED and IFN values cannot be independently interpreted or reproduced, and the aggregate score cannot be validated from the table. This missing cell is especially problematic because the paper argues HDBScan generates 'a moderate number of services.' Please supply the service counts or explain why they are unavailable.

minor comments (6)

[General] The text contains a formatting typo: 'T able 1' on the page before the table. Please fix.
[§2.2, Eq. (2)–(3)] Notation is inconsistent between metrics: Eq. (2) uses N for the total number of microservices and also as the summation index upper bound, while Eq. (3) uses M for the same concept. Also, Eq. (3) as written defines pairwise ICP_{i,j}, but the table reports a single ICP value per tool/dataset; please clarify how the pairwise values are aggregated.
[§2.2, Eq. (4)] The NED formula uses |N| in the denominator but N elsewhere, and the 'non-extreme' size range 5 ≤ |k| ≤ 20 is asserted without justification. Please clarify the notation and cite/explain the choice of range.
[§3] The sentence 'All experiments were executed on multiple developer workstations' should be scoped, since only CHGNN and MonoEmbed were actually executed in this study. As written it suggests all tools were run locally, which contradicts the later statement that values for the remaining tools were taken from papers.
[§2.3, Table 1] The Plants benchmark is described as 'a simplified variant derived from PlantsByWebSphere' but no repository URL or version identifier is given. Since reproducibility is a stated goal, please provide the exact dataset version and preprocessing steps.
[§3, Eq. (5)] The z-score normalization is described as being computed 'across all tools on the same benchmark,' but the table does not report the per-benchmark means and standard deviations. Adding these would make the aggregate score auditable.

Circularity Check

0 steps flagged · score 1.0 of 10

No constructional circularity; central claim is an empirical ranking with disclosed comparability limits.

full rationale

The paper's central claim is an empirical comparison, not a derivation from first principles. The headline that HDBScan 'produce[s] the most consistently balanced decompositions' is obtained by z-scoring the metric cells in Table 1 and applying the fixed aggregate Score(T) with W={3,-1,-1,-1}. No metric is defined in terms of the Score, and the Score is not fitted to make HDBScan win; it is applied uniformly to all tools. The main concerns are comparability and independence, not circularity: most metric values, including HDBScan's decisive IFN/ICP cells, are imported from prior papers rather than recomputed, and the weighting convention is taken from [20], the paper introducing MonoEmbed (one of the evaluated tools). These are real limitations, and the paper itself flags them: 'The evaluation combines metric values reported in prior work with metrics recomputed under our pipeline, which may affect comparability due to differences in implementations, configurations and dataset versions, particularly for ICP and IFN' (Section 5). However, importing values from another paper and adopting a weighting scheme from the literature do not make the derivation circular unless the imported values or weights are themselves defined in terms of the present conclusion, which is not the case here. No equation reduces to another by construction, and no fitted parameter is renamed as a prediction. The score of 1 reflects the mild provenance/transparency issue of using an evaluated tool's paper for the scoring rubric, not a circular-step finding.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new theoretical entities are introduced. The central claim depends on free methodological choices (the weight vector and the NED size threshold) and on domain assumptions about metric comparability and benchmark representativeness, rather than on invented physical or architectural constructs.

free parameters (2)

Aggregate score weight vector W = SM=3, IFN=-1, ICP=-1, NED=-1
Chosen by hand in Section 3 (Equation 5), following but modifying [20]. Rankings shift if weights change; no sensitivity analysis is provided.
NED non-extreme class-size range = 5 <= |k| <= 20
Adopted from [10,19] into Equation 4. This externally defined threshold controls NED scores and therefore the aggregate ranking; no sensitivity analysis is provided.

assumptions (4)

domain assumption SM, IFN, ICP and NED are valid, commensurate proxies for decomposition quality.
Section 2.2 motivates each metric; Section 3 aggregates them into a single score. If the metrics are not commensurate, the ranking is meaningless.
domain assumption Published metric values from different tools use compatible definitions, benchmark versions, and configurations.
Section 3 uses table cells from [8,10,12-14,18,21,22] as if comparable; Section 5 explicitly concedes this may not hold.
domain assumption The four selected monoliths are representative enough to generalize about tool quality.
Section 2.3 selects four benchmarks and excludes the ground-truth dataset TrainTicket among others; no evidence of representativeness is given.
domain assumption Heterogeneous execution workstations do not affect decomposition quality metrics.
Section 3 says runtime is out of scope and experiments ran on multiple developer workstations; this assumes the quality metrics are hardware-independent.

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Cite this review

Pith. "Pith review of From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks." pith.science (2026). https://pith.science/paper/KZ35QXIG

@misc{pith2026260123141,
  author       = {Pith},
  title        = {Pith review of: From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KZ35QXIG}},
  note         = {Machine review of arXiv:2601.23141}
}

read the original abstract

Software modernisation through the migration from monolithic architectures to microservices has become increasingly critical, yet identifying effective service boundaries remains a complex and unresolved challenge. Although numerous automated microservice decomposition frameworks have been proposed, their evaluation is often fragmented due to inconsistent benchmark systems, incompatible metrics, and limited reproducibility, thus hindering objective comparison. This work presents a unified comparative evaluation of state-of-the-art microservice decomposition approaches spanning static, dynamic, and hybrid techniques. Using a consistent metric computation pipeline, we assess the decomposition quality across widely used benchmark systems (JPetStore, AcmeAir, DayTrader, and Plants) using Structural Modularity (SM), Interface Number(IFN), Inter-partition Communication (ICP), Non-Extreme Distribution (NED), and related indicators. Our analysis combines results reported in prior studies with experimentally reproduced outputs from available replication packages. Findings indicate that the hierarchical clustering-based methods, particularly HDBScan, produce the most consistently balanced decompositions across benchmarks, achieving strong modularity while minimizing communication and interface overhead.

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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

Structural Validation of LLM-Generated Microservice Decompositions Using Source-Code Dependencies
cs.SE 2026-07 conditional novelty 5.0 of 10

After normalizing class-to-service mapping coverage, OpenAI o3 zero-shot and few-shot decompositions show identical structural adherence (TPD 68% PetClinic, 83.3% Bookstore).

Reference graph

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Reviewed August 3, 2026 · model on record in the stance chip above.

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arXiv preprint arXiv:2402.08481 (2024), URLhttps://arxiv.org/abs/2402.08481

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Journal of Systems and Software203, 111704 (2023)

Abgaz, Y., McCarren, A., Elger, P., et al.: Decomposition of Monolith Ap- plications Into Microservices Architectures: A Systematic Review. Journal of Systems and Software203, 111704 (2023)

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