Replication package for A Unified Cross-Language Intermediate Representation for Infrastructure-as-Code Smell Detection
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| Format: | Recurso digital |
| Langue: | anglais |
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2026
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| contents | <p><strong>General Description</strong>:</p> <p>This release includes the four IaC full dataset, the four IaC oracle dataset, the code for IRIS described in the paper, and the prompt used for the zero-shot LLM evaluation. All the results are described in our paper. All required artifacts are attached to this release.</p> <p>Users can download the project and run the main.py in the Code folder to test IaCIR on the IaC dataset. </p> <p>Users should read the README.md files in each folder to understand the purpose of each file within the folder.</p> <p> </p> <p><strong>Note for the Tables & Figures</strong>:</p> <div> <p>The complete IaC template for Figure 1 can be found in the "figure" folder of this package.</p> <p><strong>For Tables 5 and 6 in the paper, the data in each cell represents [True Positive Count (Precision, Recall)]</strong>. For instance, 2 (1.00, 0.50) indicates that two true positive cases are being detected with a precision of 1.00 and a recall of 0.50.</p> </div> <div> </div> <div> </div> <p><strong>Package Structure</strong>:</p> <p>You can check the datasets under the "data" folder.</p> <p>You can check the code for IRIS under the "code" folder.</p> <p>You can check the complete motivation example in the "figure" folder.</p> <p> </p> <div><strong>All our experimental results are publicly available on this page. For simplicity, we provide the following instructions for our artifacts:</strong></div> <ul> <li><strong>RQ1_ontology</strong> <strong>[code/evaluation/ontology/]</strong> - stores the ontology, IR construct set, and mapping result.</li> <li><strong>RQ1_efficiency</strong> <strong>[code/evaluation/result/]</strong> - stores the evaluation result CSV files, which "parse_time" column shows how long the IR conversation takes.</li> <li><strong>RQ2_oracle_dataset</strong> <strong>[data/oracle/]</strong> - stores the oracle dataset and the ground truth for the four IaC languages.</li> <li><strong>RQ2_static_analysis_tools</strong> <strong>[code/evaluation/static_analysis_tools/] </strong>- stores the result for the static analysis tool evaluation on the oracle dataset. Please note the data used in RQ2 stores i</li> <li><strong>RQ2_IRIS</strong> <strong>[data/oracle/result/] </strong>- stores the result for the IRIS run on oracle dataset.</li> <li><strong>RQ2_opus4.6</strong> <strong>[code/llm/llm_eval_outputs_xxx/] </strong>- stores the LLM results for each IaC language, where xxx is the placeholder for the IaC language name.</li> <li><strong>RQ3_dataset [data/xxx_collected_template_new/]</strong> - stores the dataset for each IaC language, where xxx is the placeholder for the IaC language name.</li> <li><strong>RQ3_result [code/evaluation/result/] </strong>- stores the smell detection results by IRIS on the full dataset for each IaC language (data/xxx_collected_template_new). Please note that the data used in RQ1_efficiency and RQ2_IRIS are taken from these files.</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19345283 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Replication package for A Unified Cross-Language Intermediate Representation for Infrastructure-as-Code Smell Detection Anonymous IaC DevOps Dataset Oracle dataset Smell Detection <p><strong>General Description</strong>:</p> <p>This release includes the four IaC full dataset, the four IaC oracle dataset, the code for IRIS described in the paper, and the prompt used for the zero-shot LLM evaluation. All the results are described in our paper. All required artifacts are attached to this release.</p> <p>Users can download the project and run the main.py in the Code folder to test IaCIR on the IaC dataset. </p> <p>Users should read the README.md files in each folder to understand the purpose of each file within the folder.</p> <p> </p> <p><strong>Note for the Tables & Figures</strong>:</p> <div> <p>The complete IaC template for Figure 1 can be found in the "figure" folder of this package.</p> <p><strong>For Tables 5 and 6 in the paper, the data in each cell represents [True Positive Count (Precision, Recall)]</strong>. For instance, 2 (1.00, 0.50) indicates that two true positive cases are being detected with a precision of 1.00 and a recall of 0.50.</p> </div> <div> </div> <div> </div> <p><strong>Package Structure</strong>:</p> <p>You can check the datasets under the "data" folder.</p> <p>You can check the code for IRIS under the "code" folder.</p> <p>You can check the complete motivation example in the "figure" folder.</p> <p> </p> <div><strong>All our experimental results are publicly available on this page. For simplicity, we provide the following instructions for our artifacts:</strong></div> <ul> <li><strong>RQ1_ontology</strong> <strong>[code/evaluation/ontology/]</strong> - stores the ontology, IR construct set, and mapping result.</li> <li><strong>RQ1_efficiency</strong> <strong>[code/evaluation/result/]</strong> - stores the evaluation result CSV files, which "parse_time" column shows how long the IR conversation takes.</li> <li><strong>RQ2_oracle_dataset</strong> <strong>[data/oracle/]</strong> - stores the oracle dataset and the ground truth for the four IaC languages.</li> <li><strong>RQ2_static_analysis_tools</strong> <strong>[code/evaluation/static_analysis_tools/] </strong>- stores the result for the static analysis tool evaluation on the oracle dataset. Please note the data used in RQ2 stores i</li> <li><strong>RQ2_IRIS</strong> <strong>[data/oracle/result/] </strong>- stores the result for the IRIS run on oracle dataset.</li> <li><strong>RQ2_opus4.6</strong> <strong>[code/llm/llm_eval_outputs_xxx/] </strong>- stores the LLM results for each IaC language, where xxx is the placeholder for the IaC language name.</li> <li><strong>RQ3_dataset [data/xxx_collected_template_new/]</strong> - stores the dataset for each IaC language, where xxx is the placeholder for the IaC language name.</li> <li><strong>RQ3_result [code/evaluation/result/] </strong>- stores the smell detection results by IRIS on the full dataset for each IaC language (data/xxx_collected_template_new). Please note that the data used in RQ1_efficiency and RQ2_IRIS are taken from these files.</li> </ul> |
| title | Replication package for A Unified Cross-Language Intermediate Representation for Infrastructure-as-Code Smell Detection |
| topic | IaC DevOps Dataset Oracle dataset Smell Detection |
| url | https://doi.org/10.5281/zenodo.19345283 |