Fine-Grained Project Evaluation Framework

• The paper introduces a novel evaluation framework that decomposes complex projects into atomic components to provide precise, component-level diagnostics.
• It employs modular design and automated benchmarks—such as TransRepo-bench and LEGO-Bench—to assess metrics across software translation, 3D synthesis, and metadata quality.
• The framework enables iterative refinement through isolated unit evaluations, offering actionable insights to improve performance and reproducibility in diverse domains.

A fine-grained project evaluation framework is a systematic methodology for assessing the quality and functionality of complex systems and outputs—such as software repositories, generated artifacts, or data portals—at a level of granularity that allows pinpointing partial successes and localized failures. These frameworks contrast with coarse, binary, or aggregate metrics by providing multi-dimensional, per-component or per-constraint insights, thus enabling targeted feedback, diagnostic precision, and actionable benchmarking across domains including code translation, machine learning for software engineering, open data, 3D scene synthesis, and visual content generation (Zhang et al., 27 Jan 2025, Bogomolov et al., 2022, Hwangbo et al., 4 Nov 2025, Wenige et al., 2021, Chen et al., 16 Sep 2025).

1. Rationale and Foundational Principles

Fine-grained evaluation frameworks originate from the need to overcome the limitations of coarse “all-or-nothing” metrics. In code translation, a single build error in a large monolithic repository can obscure the successful translation of independent modules. In generated text or scene synthesis, holistic similarity scores fail to identify which specific requirements are fulfilled versus violated. Fine-grained frameworks explicitly decompose the evaluation space, mapping atomic requirements, constraints, or test cases to independent, interpretable metrics.

Key principles include:

• Decomposition: Explicit identification and labeling of evaluation units (e.g., unit tests, instruction constraints, metadata fields).
• Isolation: Testing or measuring each evaluation unit independently of others to avoid cascading failure effects.
• Modularity: Support for plugging in specialized tools, metrics, or evaluators per dimension (Chen et al., 16 Sep 2025, Hwangbo et al., 4 Nov 2025).
• Automation and Repeatability: Fixed test artifacts and evaluation harnesses for rapid iteration, as exemplified by TransRepo-bench and EdiVal-Bench (Zhang et al., 27 Jan 2025, Chen et al., 16 Sep 2025).

2. Framework Architectures and Processes

2.1 Skeleton-Guided Translation (Code Repositories)

The “Skeleton-Guided-Translation” framework evaluates repository-level code translation by a two-phase process:

• Phase I: Skeleton Extraction & Translation—Extract method signatures and structure, strip bodies, and translate this skeleton to establish a type-correct, dependency-respecting template in the target language (e.g., Java→C#). This isolates the architectural contract.
• Phase II: Guided Full Translation—Populate skeleton method bodies incrementally, prompting an LLM per file/class and correcting errors per unit, thus supporting fine-grained assessment at each code/test level.

Pseudocode formalization (cf. (Zhang et al., 27 Jan 2025)): $\begin{algorithmic}[1] \Procedure{TranslateRepository}{JavaRepo, C\#Skeleton} \State Skeleton %%%%0%%%% ExtractSkeleton(JavaRepo) \State CLangSkeleton %%%%1%%%% LLM\_Translate(Skeleton, \text{"Java→C\# skeleton"}) \State FixCompilationErrors(CLangSkeleton) \ForAll{source file %%%%2%%%% in JavaRepo} \State newBodies %%%%3%%%% LLM\_Translate(\text{Prompt}(f)) \State InsertBodies(CLangSkeleton, f, newBodies) \State FixErrors(CLangSkeleton) \EndFor \State \Return CLangSkeleton \EndProcedure \end{algorithmic}$

2.2 Project-Specific ML4SE Evaluation

Per-project frameworks in ML4SE (e.g., method name prediction) use commit history mining to create strict chronological train/validation/test splits within a project, supporting “future-blind” fine-tuning and performance measurement on post-snapshot innovations (Bogomolov et al., 2022). Three model regimes are compared: original (cross-project pretrained), project-only (trained from scratch), and fine-tuned (pretrained then project-specialized).

2.3 Constraint-Centric Evaluation (3D Scenes, Image Editing)

Frameworks such as LEGO-Eval and EdiVal-Agent decompose complex generated outputs into atomic constraints or objects, pairing each with a dedicated evaluation pipeline (constraint parsing, execution planning, external tool invocation, validator module). Each constraint’s satisfaction or violation is recorded—together with an interpretable rationale—enabling partial and holistic performance metrics (Hwangbo et al., 4 Nov 2025, Chen et al., 16 Sep 2025).

2.4 Open Data and Metadata Quality

For Open Data portals, units of analysis include fields such as interoperability, findability, and completeness—each quantified according to field-level presence, normalization, or cross-portal uniqueness. All scoring logic is made explicit via mathematical formulas, supporting both per-dimension and aggregated dashboard-style reporting (Wenige et al., 2021).

3. Fine-Grained Metric Design and Formulation

The core of a fine-grained evaluation framework lies in its metric suite, enabling continuous-valued, partial, and conditional outcomes. Examples include:

3.1 Code Repository Translation (Zhang et al., 27 Jan 2025)

Let $\mathcal{T}=\{\tau_1,\dots,\tau_N\}$ be the set of unit tests. Then,

• Build Success Rate:

$$R_{\mathrm{build}} = \frac{1}{N} \sum_{i=1}^{N} \mathbb{1}_{\mathrm{build}(\tau_i)}$$

• Unit-Test Pass Rate:

$$R_{\mathrm{pass}} = \frac{1}{N} \sum_{i=1}^{N} \mathbb{1}_{\mathrm{pass}(\tau_i)}$$

• Conditional Pass-Given-Build:

$$R_{\mathrm{cond}} = \frac{\sum_{i} \mathbb{1}_{\mathrm{pass}(\tau_i)}}{\sum_{i} \mathbb{1}_{\mathrm{build}(\tau_i)}} \quad \text{if } \sum_{i} \mathbb{1}_{\mathrm{build}} > 0$$

3.2 Constraint Satisfaction (3D Scene Synthesis, Visual Editing)

For $k$ constraints $C=\{c_1,\ldots,c_k\}$:

• Per-constraint Binary Scores: $y_i = 1[c_i \text{ satisfied in } S]$
• Holistic Score: $J = 1$ if $\sum_i y_i = k$, else $0$
• Precision, Recall, $F_1$ (partial and holistic), Cohen’s $\kappa$: all standard, but applied at both atomic and aggregate scopes (Hwangbo et al., 4 Nov 2025, Chen et al., 16 Sep 2025).

3.3 Open Data

• Uniqueness for property $t$:

$$\text{uniqueness}_t = \frac{\log \bigl(1 + \frac{t_f − v_f + 0.5}{v_f + 0.5}\bigr)}{\log \bigl(1 + \frac{t_f − 1 + 0.5}{1.0 + 0.5}\bigr)}$$

• Replica Ratio, Accessibility Ratio, and other field-level metrics as per dataset schema (Wenige et al., 2021).

3.4 ML4SE Per-Method Evaluation

• F1, ChrF, Bootstrap Significance: All metrics reported per-project, not just cross-project (Bogomolov et al., 2022).

4. Comparative Analysis and Case Studies

Fine-grained frameworks decisively reveal granular capabilities and failure modes invisible to coarse metrics:

• Repository Translation: In the double-buffer case paper, a single misnamed method (draw) invalidated an entire build under a binary metric; fine-grained per-test analysis surfaced $6/10$ passing tests and $8/10$ compiling code blocks, enabling targeted debugging (Zhang et al., 27 Jan 2025).
• 3D Scene Synthesis: In LEGO-Eval’s benchmark, partial constraint satisfaction rates (~60%) differ dramatically from holistic success (≤10%), showing generation models capture many subgoals but rarely all simultaneously (Hwangbo et al., 4 Nov 2025).
• Image Editing: EdiVal-Agent isolates failures in object-level instruction following versus content drift, clearly differentiating spatial errors, exposure bias, and attribute hallucinations across editing paradigms (Chen et al., 16 Sep 2025).
• Open Data: Portal-level analysis uncovers field-specific and cross-portal inefficiencies, such as low DCAT-Ratio (13% of German ODPs offering DCAT natively) and low accessibility (36.9% accessURLs returning HTTP 200) (Wenige et al., 2021).
• ML4SE: Project-level fine-tuning yields $+6.7$ pp $F_1$ improvement on large projects for CodeTransformer, with statistical win rates reported per project (Bogomolov et al., 2022).

5. Benchmark Construction and Automation

Fine-grained frameworks are characterized by careful benchmark design to ensure reproducibility and incremental improvement:

• TransRepo-bench: Curates open-source Java repositories with ≥100 stars, high test coverage, and fixed, buildable unit tests synchronized with translated skeleton interfaces. Automatic Docker-based workflows permit repeated model benchmarking with no manual test retuning (Zhang et al., 27 Jan 2025).
• LEGO-Bench: Annotator-curated 3D scene–instruction pairs, atomic constraint annotation, and tool-based scoring pipelines. 130 detailed instructions, 1,250 constraints, and extensive category labeling enable comprehensive partial and holistic analysis (Hwangbo et al., 4 Nov 2025).
• EdiVal-Bench: Multi-turn image editing instances with object-level decomposition, covering nine edit types and 11 models, with all evaluation steps modularized for tool substitution or extension (Chen et al., 16 Sep 2025).

6. Design Lessons, Limitations, and Extensions

Several lessons emerge consistently:

• Skeletons/abstractions enable isolation: Without skeletal contract establishment, interdependency errors swamp both build rates and test pass rates.
• Iterative refinement: Automated, tool-driven feedback loops systematically eliminate syntactic/structural errors, yet leave deeper logic or semantic faults challenging (Zhang et al., 27 Jan 2025).
• Automation: Fixed, versioned test suites synchronized to project skeletons or APIs enable benchmarking over time and across models, decoupled from model-specific quirks.
• Diagnostic Power: Fine-grained frameworks surface recoverable/partial successes and actionable error localization, supporting both evaluation and model development.
• Modularity and extensibility: Separation of constraint parsing, planning, tool invocation, and validation supports future expansion and adaptation across domains, with minimal evaluation infrastructure rewrite (Hwangbo et al., 4 Nov 2025, Chen et al., 16 Sep 2025).
• Limitations: Model dependence persists; open-source evaluation agents may lag state-of-the-art commercial models. Complex dependency structures or non-standard geometries remain challenging.

A plausible implication is that the widespread adoption and further development of fine-grained project evaluation frameworks will enable both more robust experimental methodology and faster progress in automated system synthesis, migration, and evaluation.

7. Domain-Specific Frameworks: Comparative Table

Framework	Domain	Core Granularity	Key Metrics/Units
Skeleton-Guided-Translation / TransRepo-bench (Zhang et al., 27 Jan 2025)	Code repo translation	Unit test / method	Per-test build/pass, incremental repair
LEGO-Eval / LEGO-Bench (Hwangbo et al., 4 Nov 2025)	3D scene synthesis	Atomic instruction constraints	Partial/holistic F1, constraint rationale
EdiVal-Agent / EdiVal-Bench (Chen et al., 16 Sep 2025)	Image editing	Object/turn/instruction	Turn-wise success, consistency, quality
ML4SE Project Evaluation (Bogomolov et al., 2022)	Method name prediction	Project/time-split method	F1, ChrF per-project, bootstrapping
Open Data (Wenige et al., 2021)	Metadata quality	Field / dataset	Uniqueness, completeness, accessibility

These frameworks collectively illustrate the unifying concept of fine-grained project evaluation across modalities, with implementations adapted to the structural and practical intricacies of each application domain.