README.md

ThrowGen: datasets and results

This archive contains the evaluation datasets, model predictions, and evaluation results behind the numbers, tables, and figures reported in the ThrowGen paper. It holds the artifacts the reported results are computed from, and nothing else.

published_data/
  data/throwgen/<dataset>/         # evaluation and validation datasets
  results/llm_output/<dataset>/    # raw LLM predictions
  results/metrics/<dataset>/       # evaluation metrics
  results/qualitative/             # manual qualitative analysis
  MANIFEST.txt                     # every file in this archive, one per line

Datasets (data/throwgen/)

Six of the seven datasets trace the construction funnel described in the dataset section of the paper; the seventh is the validation set used for prompt selection.

Dataset	Methods	Role
real-non-direct-mega-test-data	1,099	Initial collection from GitHub projects
real-mega-test-data	546	After keeping exceptions thrown directly in the target method
real-mega-test-data-with-exception	525	After requiring the exception type to match the test
real-mega-test-data-with-exception-with-project-with-gold	399	After requiring the developer implementation to pass its own tests
real-mega-test-data-with-exception-with-project-with-gold-with-throw	304	Evaluation set — all results below are on this set
mega-val-data-with-exception-with-project-with-gold-with-throw	214	Validation set
all-data	518	Evaluation and validation sets combined

Each dataset is stored as one JSON Lines file per field, aligned row by row: mut (the target method), mut_no_throw (the method with exception-raising code removed, which is the task input), ebts (exception-behavior tests), nebts (non-exceptional tests), randoop_tests and evosuite_tests (tool-generated tests), and the contextual fields class_info, method_info, throw_info, import_info, local_variable_type, coverage, thrown_exception, and unreported_exception. id.jsonl, project.jsonl, start_line.jsonl, and end_line.jsonl locate each method in its source project. dataset_stats.json and throw-count.json carry the aggregate statistics reported in the dataset tables.

Models and prompt configurations

Five models, all evaluated on the 304-method evaluation set, 10 samples per method at temperature 0.8:

• llama3.1:8b-instruct-q8_0 (llama_cpp)
• phi4:14b-q8_0 (llama_cpp)
• qwen2.5-coder:7b-instruct-q8_0 (llama_cpp)
• qwen2.5-coder:32b-instruct-q8_0 (llama_cpp) — the main model
• gpt-5-mini (azure)

Prompt configurations:

• base and tuctn-all-info for all five models, for the model comparison
• cmtu, only-avsym, only-lcov, only-nebt, and only-threxc for the main model, for the prompt comparison and context ablations
• base-repair@1..4 and tuctn-all-info-repair@1..4 for the main model, for the iterative self-repair experiment

Results

results/llm_output/ — one JSON Lines file per model and prompt configuration, holding the raw generations for each method.

results/metrics/ — for each model and prompt configuration, a -summary.json (aggregate) and a -each-sample.jsonl (per method) for the three evaluation types the paper reports:

• run-ebts-pass-at-k — compile@k and pass@k on the developer-written exception-behavior tests
• run-all-pass-at-k — pass@k on those tests together with the non-exceptional tests
• run-all-with-tools-pass-at-k — the above plus the Randoop and EvoSuite generated tests

Two additional per-sample files, run-all-llama_cpp-qwen2.5-coder:32b-instruct-q8_0-base-multi_ebt-each-sample.jsonl and its tuctn-all-info counterpart, record which individual methods pass under each prompt; they are the source of the Venn diagram and the overlap counts in the results section.

File names follow <evaluation-type>-<backend>-<model>-<prompt>-<setup>-{summary.json,each-sample.jsonl}, where setup is multi_ebt for the main experiments and repair for the self-repair iterations.

results/qualitative/ — the manual analysis behind the qualitative section: per-sample labels and summaries for base and tuctn-all-info, and the inter-annotator agreement statistics. Files prefixed same_samples_ restrict the analysis to the methods labeled under both prompt configurations.

Reproducing the reported numbers

Every aggregate metric in this archive was checked against the corresponding value printed in the paper:

• run-ebts-pass-at-k — all 138 values match.
• run-all-pass-at-k — all 138 values match.
• run-all-with-tools-pass-at-k — 30 of 90 values match. The four only-* context ablations agree exactly; the remaining eleven model and prompt combinations differ by 0.46 percentage points on average and 1.95 at worst (qwen2.5-coder:32b-instruct-q8_0 with tuctn-all-info, compile@1). The tool-augmented evaluation was re-run after those numbers were typeset, so the values here supersede the ones printed in the paper.

One column is not reproducible from this archive: the tool-augmented pass@k figures for the self-repair iterations. The per-iteration run-all-with-tools-pass-at-k summaries for the repair setup are unavailable, so only the run-ebts-pass-at-k and run-all-pass-at-k columns of the self-repair table can be regenerated here.