## 5 Validation
### Datasets  
In addition to the ACMG process and its architecture, the main contribution of this study is the datasets we created. All datasets were created manually in collaboration with domain experts in the field of MiniZinc and/or telecommunication equipment configurations. Each dataset was preprocessed into jsonl format, with prompts and completions standardized to avoid truncation or padding mismatches. OpenAI's tokenizer automatically managed subword segmentation, ensuring consistent input encoding. No additional tokenization normalization was applied, as the pipeline's built-in methods align with the models' pretraining standards.  
Domain experts annotated the dataset using guidelines covering entity extraction (variables, domains, constraints) and MiniZinc translation. Inter-annotator agreement was measured (\(\kappa\) = 0.82), and all MiniZinc models were validated for syntax/solution correctness. Discrepancies were resolved via consensus.  
We produced the following datasets:  
CSPNERMZC-50: This dataset [19] comprises 50 constraint satisfaction problems described in natural language with manually annotated semantic entities (input parameters, variables, domains, and constraints) and corresponding MiniZinc code. The dataset consists of seven columns:  
* The model and constraint task are described in natural language.
* Model only.
* Constraint task only.
* Model-relevant semantic entities.
* Constraint task-relevant semantic entities.
* MiniZinc model (mzn file).
* MiniZinc constraint task (dzn file).  
The segmentation of the CSPNERMZC-50 dataset into smaller subsets is presented in Figure 11. These smaller datasets were used to build the different datasets described in the remainder of this subsection (e.g., CSPNER-50 is a combination of CSP-50 and NER-50, as shown in Figure 11).  
CSP-50: This dataset [19] comprises 50 constraint satisfaction problems described in natural language. The dataset consists of three columns:  
* The model and constraint task are described in natural language.
* Model only.
* Constraint task only.  
CSPNER-50: This dataset [19] comprises 50 constraint satisfaction problems described in natural language with manually annotated semantic entities (input parameters, variables, domains, and constraints). The dataset consists of five columns:  
Figure 11: Segmentation of the created CSPNERMZC-50 dataset into subsets.  
* The model and constraint task are described in natural language.
* Model only.
* Constraint task only.
* Model-relevant semantic entities.
* Constraint task-relevant semantic entities.  
MZC-50: This dataset [19] comprises 50 MiniZinc constraint models, their natural language descriptions, and the generated MiniZinc code. The dataset consists of five columns:
* The model and constraint task are described in natural language.
* Model only.
* Constraint task only.
* MiniZinc model (mzn file).
* MiniZinc constraint task (dzn file).  
VAL-10: This dataset consists of 10 constraint satisfaction problems that are used for validation [51].  
In addition to the four datasets described above, we also used the dataset in [16] and the Mixed CP Dataset from [17] for benchmarking; however, the dataset in [16] only contains arbitrary tasks, so we discarded it from our results. As the Mixed CP Dataset comprises 13 CSPs and 5 COPs, we only used its CSPs.