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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'Owner (Partner)', 'state', 'Requirement ID', 'Title'}) and 5 missing columns ({'Related KIO', 'Requirement Type', 'Rationale', 'Requirement ID Nr.', 'Requirement Name'}).

This happened while the json dataset builder was generating data using

hf://datasets/AI4SWEng/d9.2_requirements_appendix/d9.2-functional-23-6-2026.json (at revision 8d7fb25f33f9c3b5104bfdcaaadba3d935eed236), ['hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d2.4-requirements.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-functional-23-6-2026.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-non-functional-23-6-2026.json'], ['hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d2.4-requirements.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-functional-23-6-2026.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-non-functional-23-6-2026.json']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              Title: string
              Owner (Partner): string
              Priority: string
              Requirement ID: string
              state: string
              to
              {'Related KIO': Value('string'), 'Requirement Name': Value('string'), 'Requirement Type': Value('string'), 'Requirement ID Nr.': Value('string'), 'Priority': Value('string'), 'Rationale': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'Owner (Partner)', 'state', 'Requirement ID', 'Title'}) and 5 missing columns ({'Related KIO', 'Requirement Type', 'Rationale', 'Requirement ID Nr.', 'Requirement Name'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/AI4SWEng/d9.2_requirements_appendix/d9.2-functional-23-6-2026.json (at revision 8d7fb25f33f9c3b5104bfdcaaadba3d935eed236), ['hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d2.4-requirements.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-functional-23-6-2026.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-non-functional-23-6-2026.json'], ['hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d2.4-requirements.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-functional-23-6-2026.json', 'hf://datasets/AI4SWEng/d9.2_requirements_appendix@8d7fb25f33f9c3b5104bfdcaaadba3d935eed236/d9.2-non-functional-23-6-2026.json']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Related KIO string	Requirement Name string	Requirement Type string	Requirement ID Nr. string	Priority string	Rationale string
KIO1	Unified KIO Integration Platform	Functional	FR-KIO1-01	Mandatory	Core functionality
KIO1	Front-End GUI for Lifecycle Interaction	Functional	FR-KIO1-02	Mandatory	Core functionality
KIO1	DevOps Workflow Orchestration	Functional	FR-KIO1-03	Mandatory	Core functionality
KIO1	Containerized Service Deployment	Functional	FR-KIO1-04	Mandatory	Core functionality
KIO1	Horizontal LLM Integration	Functional	FR-KIO1-05	Mandatory	Core functionality
KIO2	Source code language support (One language for PoC)	Functional	FR-KIO2-01	Mandatory	Essential for diagnostic workflow
KIO2	Trace Capture & Replay to generate reversible trace data	Functional	FR-KIO2-02	Mandatory	Essential for diagnostic workflow
KIO2	AI-assisted replay simulators that reconstruct previous states from logged diffs	Functional	FR-KIO2-03	Mandatory	Essential for diagnostic workflow
KIO2	Dynamic slicing to identify which statements and data dependencies contributed to a given program behaviour	Functional	FR-KIO2-04	Mandatory	Essential for diagnostic workflow
KIO2	AI Fault Localization to train from replayed traces and slice context	Functional	FR-KIO2-05	Mandatory	Improves speed or convenience
KIO2	The system shall employ a specialised Large Language Model (LLM) fine-tuned for bug detection to automatically identify, classify, and prioritise software faults from source code and execution trace	Functional	FR-KIO2-06	Optional	Improves speed or convenience
KIO2	The system shall automatically generate, rank, and validate fix suggestions for detected faults using AI/LLM-based reasoning, ensuring each proposed patch compiles successfully, preserves intended functionality, and provides an explainable rationale for developer review.	Functional	FR-KIO2-07	Optional	Value-add feature
KIO2	Thread-aware trace capture and replay simulation	Functional	FR-KIO2-08	Desired	Value-add feature
KIO3	Parse and extract key intent and actors from natural language	Functional	FR-KIO3-01	Mandatory	Core outcomes
KIO3	Generate formalized user stories using a trained LLM	Functional	FR-KIO3-02	Mandatory	General improvement
KIO3	Attach acceptance criteria to each story	Functional	FR-KIO3-05	Mandatory	Core outcomes
KIO3	Allow post-generation editing by users	Functional	FR-KIO3-08	Desirable	Improves quality
KIO3	Detect and highlight ambiguous or vague input	Functional	FR-KIO3-02.b	Desirable	Improves quality
KIO3	Automated wireframe generation	Functional	FR-KIO3-04 (new requirement)	Desirable	Productivity booster
KIO3	Export functionality for project artifacts	Functional	FR-KIO3-07 (new requirement)	Mandatory	Enables interoperability
KIO4	Parse formal requirements and identify architectural implications	Functional	FR-KIO4-02	Mandatory	Core deliverable
KIO4	Suggest high-level architecture	Functional	FR-KIO4-06	Mandatory	Core deliverable
KIO4	Generate UML diagrams	Functional	FR-KIO4-05	Desirable	Productivity booster
KIO4	Provide traceability matrix between requirements and design components	Functional	FR-KIO4-03	Desirable	Improves usability
KIO4	Allow user interaction to accept, modify, or reject suggestions	Functional	FR-KIO4-06.b	Mandatory	General improvement
KIO4	Export project scaffolding/code skeletons based on architecture	Functional	FR-KIO4-10	Desirable	Productivity booster
KIO4	Architectural Rationale Documentation and Attachment	Functional	FR-KIO4-07 (new requirement)	Desirable	General improvement
KIO4	Formal requirements ingestion	Functional	FR-KIO4-01 (new requirement)	Mandatory	Defines component input
KIO4	Architectural component export for tool integration	Functional	FR-KIO4-08 (new requirement)	Mandatory	Enables interoperability
KIO5	Generate compliant synthetic traffic at flow level	Functional	FR-KIO5-07	Mandatory	Core deliverable
KIO5	Generate synthetic traffic representing attacks at the flow level	Functional	FR-KIO5-09	Mandatory	Core deliverable
KIO5	Merge GIS layers from different sources to create a new synthetic map representing energy consumption	Functional	FR-KIO5-01 (new requirement)	Mandatory	Core deliverable
KIO5	Generate synthetic GIS layers to create new synthetic maps representing non-existing energy consumption scenarios	Functional	FR-KIO5-02 (new requirement)	Optional	Feature for later phase
KIO5	Generate a master medical datasets by merging together datasets by creating synthetic link between the entries	Functional	FR-KIO5-03 (new requirement)	Mandatory	Core deliverable
KIO5	Propose validation methods for each generation task	Functional	FR-KIO5-04 (new requirement)	Mandatory	Quality baseline
KIO5	Report generation for generation tasks	Functional	FR-KIO5-05 (new requirement)	Desirable	Essential operational quality
KIO6	Comparison between input data and properties schema	Functional	FR-KIO6-01	Desirable	General improvement
KIO6	Automated data preprocessing	Functional	FR-KIO6-06	Mandatory	Core checks
KIO6	Report generation for data validation, statistical properties, code errors, aggregate data quality	Functional	FR-KIO6-07	Desirable	Maintainability/usability
KIO6	Data preparation for LLM	Functional	FR-KIO6-08	Desirable	General improvement
KIO6	User Interface to display the tool outputs, the different steps' details and to edit the data schema	Functional	FR-KIO6-03	Desirable	Improves usability
KIO7	Accept prompts and generate code	Functional	FR-KIO7-05	Mandatory	General improvement
KIO7	Retrieve relevant code patterns or documentation (RAG)	Functional	FR-KIO7-03	Desirable	Maintainability/usability
KIO7	Fine-tune LLMs	Functional	FR-KIO7-02	Mandatory	General improvement
KIO7	Debug code and return annotated diagnostics and fixes	Functional	FR-KIO7-06	Mandatory	General improvement
KIO7	Auto-generate and execute test cases	Functional	FR-KIO7-11	Desirable	Enhanced capabilities
KIO7	Suggest performance or security improvements	Functional	FR-KIO7-07	Desirable	Enhanced capabilities
KIO7	Support multiple programming languages and frameworks	Functional	FR-KIO7-05	Desirable	General improvement
KIO7	Fine-tuned model checkpoint management	Functional	FR-KIO7-09 (new requirement)	Desirable	General improvement
KIO7	Training data ingestion	Functional	FR-KIO7-01 (new requirement)	Mandatory	Enables fine-tuning process
KIO7	Fine-tuned model export	Functional	FR-KIO7-08 (new requirement)	Mandatory	Enables MLOps integration
KIO7	Feedback response to toolchains	Functional	FR-KIO7-26	Mandatory	Key part of the workflow
KIO7	High-Level Synthesis (HLS) Programming Support	Functional	FR-KIO7-25	Mandatory	Support to LLM code generation
KIO8	Code optimisation depending on the specified architectures and softwares	Functional	FR-KIO8-01	Mandatory	General improvement
KIO8	Software reconfiguration recommendations depending on the optimised code and the choosen architecture	Functional	FR-KIO8-02	Mandatory	General improvement
KIO8	Report of the identified optimisation opportunities and the proposed changes	Functional	FR-KIO8-03	Desirable	Maintainability/usability
KIO8	Possibility to change manually some unwanted optimisations	Functional	FR-KIO8-04	Optional	General improvement
KIO8	Ensure iterative code generation via interaction with toolchain	Functional	FR-KIO8-10	Mandatory	Core functionality
KIO9	Hash-based decentralised code integrity. Security check mechanism to identify code manipulations. Preventing untrusted sources.	Functional	FR-KIO09-01, FR-KIO9-04, FR-KIO9-05	Mandatory	Security baseline
KIO9	Multifactor Authentication Mechanism for users' identity management	Functional	FR-KIO9-02	Mandatory	Security baseline
KIO9	End-to-end security enabling encrypted storage or data transmission (e.g. against MitM)	Functional	FR-KIO9-03	Mandatory	Security baseline
KIO10	Execute Machine Learning Tasks on Resource-Constrained Devices	Functional	FR-KIO10-01	Mandatory	Core functionality
KIO10	Enable ML Execution Using TinyML	Functional	FR-KIO10-02	Mandatory	Core functionality
KIO10	Use Task-Specific LLMs for On-Device Analytics	Functional	FR-KIO10-03	Optional	Core functionality
KIO10	Perform Analytics Directly on Edge Devices	Functional	FR-KIO10-04	Mandatory	Core functionality
KIO10	Apply Model-Driven Software Engineering Principles	Functional	FR-KIO10-05	Mandatory	Improves speed or convenience
KIO10	Address Complex Design Challenges with Domain-Specific Modeling	Functional	FR-KIO10-06	Mandatory	Improves speed or convenience
KIO10	Address Complex Validation Challenges with Domain-Specific Modeling	Functional	FR-KIO10-07	Desirable	Improves speed or convenience
KIO10	Generate Code for Diverse IoT Platforms	Functional	FR-KIO10-08	Optional	Improves speed or convenience
KIO10	Support Deployment on Diverse IoT Platforms	Functional	FR-KIO10-09	Mandatory	Core functionality
KIO10	Support Deployment on Ultra-Low-Power Microcontrollers	Functional	FR-KIO10-10	Mandatory	Core functionality
KIO10	Enable Battery Remaining Useful Life Prediction Use Case	Functional	FR-KIO10-11	Mandatory	Core functionality
KIO10	Integrate with TensorFlow Lite for ML Operations	Functional	FR-KIO10-12	Mandatory	Core functionality
KIO10	Integrate with Microcontroller Variant of TensorFlow Lite	Functional	FR-KIO10-13	Mandatory	Core functionality
KIO11	Automated self-healing testing to improve context awareness via contextual AI models, LLMs	Functional	FR-KIO11-01	Mandatory	General improvement
KIO11	AI-driven test case generation	Functional	FR-KIO11-02	Mandatory	Core automation capability
KIO11	Refinement of test cases (prioritation and redundancy detection, prunin if needed)	Functional	FR-KIO11-03	Desirable	General improvement
KIO11	Automated test script generation	Functional	FR-KIO11-04	Mandatory	Core automation capability
KIO11	Visualisation of TAT results (GUI)	Functional	FR-KIO11-05	Mandatory	Efficiency and insight
KIO12	AI-based security check mechanism and vulnerability analysis to identify: (i) MitM, DoS, SQL-injection, XSS, (ii) phishing, and (iii) authentication related attacks	Functional	FR-KIO12-01, FR-KIO12-04, FR-KIO12-05	Mandatory	Security baseline
KIO12	Security test scenario generation	Functional	FR-KIO12-03	Mandatory	Core security functionality
KIO12	End-to-end security enabling encrypted storage or data transmission + multifactor authentication	Functional	FR-KIO12-10	Mandatory	Core security functionality
KIO13	Ontological Input Handling	Functional	FR-KIO13-01	Mandatory	Enables structured ontological input for precise requirements framing
KIO13	Requirements Derivation	Functional	FR-KIO13-02	Mandatory	Core to deriving requirements from ontological frameworks
KIO13	Ontology Building Tools	Functional	FR-KIO13-03	Mandatory	Core to creating ontological structures for training
KIO13	Visualization Generation	Functional	FR-KIO13-04	Mandatory	Facilitates comprehension of complex ontological structures through visual aids
KIO13	Training Content Delivery	Functional	FR-KIO13-05	Mandatory	Ensures accessible and engaging delivery of training content to maximize learning outcomes
KIO13	Prompt Skills Training	Functional	FR-KIO13-06	Mandatory	Equips developers with advanced prompt engineering skills to leverage LLMs effectively
KIO13	Output Evaluation	Functional	FR-KIO13-07	Mandatory	Enhances learning depth and breadth
KIO13	Pipeline Integration	Functional	FR-KIO13-08	Desirable	Enhances learning depth and breadth
KIO13	Progress Monitoring	Functional	FR-KIO13-09	Mandatory	Community feature for later phase
KIO13	Impact Reporting	Functional	FR-KIO13-10	Desirable	Enhances learning depth and breadth
KIO13	Interactive Learning Modules	Functional	FR-KIO13-11	Mandatory	Support interactive simulations for hands-on prompt engineering practice
KIO13	Full user companion guide	Functional	FR-KIO13-12	Mandatory	Provides comprehensive guidance on configuration, integration, and prompt engineering to ensure effective use of the AI-powered training platform, enhancing developer productivity and adoption
KIO1	Modular Architecture Design	Non-Functional	NFR-KIO1-01	Mandatory	Critical quality attribute for enterprise adoption
KIO1	Flexibility in Workflow Configuration	Non-Functional	NFR-KIO1-02	Mandatory	Critical quality attribute for enterprise adoption
KIO1	Scalability for Integrated Services	Non-Functional	NFR-KIO1-03	Mandatory	Critical quality attribute for enterprise adoption
KIO1	Interoperability with External Systems	Non-Functional	NFR-KIO1-04	Desirable	Critical quality attribute for enterprise adoption
KIO1	Security in Integration Layer	Non-Functional	NFR-KIO1-05	Mandatory	Ensures secure integration of services to protect sensitive data and maintain trust
KIO1	System Reliability	Non-Functional	NFR-KIO1-06	Mandatory	System must maintain 99.9% uptime to support continuous DevOps workflows

End of preview.

Details

This dataset contains the structured JSON files for the functional and non-functional requirements of Deliverable D9.2, alongside the baseline requirements from the previous D2.4 deliverable.

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