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MuTAP

MuTAP is a prompt-based learning technique to generate effective test cases with Large Language Models (LLMs). This is an implementation of a method described in Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing.

MuTAP starts by calling an initial prompt on LLM (Codex and llama-2-chat) to generate test cases for a Program Under Test (PUT). The initial prompt uses zero-shot or few-shot learning techniques. Then, MuTAP attempts to repair the syntax and functional errors within the test cases generated by Codex. After generating the test cases, MuTAP attempts to identify and repair any syntax or functional errors. Once these issues are fixed, MuTAP generates different mutants for the PUT and calculates the Mutation Score (MS) of the test cases. If there are any surviving mutants, MuTAP uses them to augment the initial prompt and re-prompt Codex to improve its own output by generating a new test case that can kill the surviving mutants.

1. Initial prompt on LLMC (Codex and Llama-2-chat) and syntax Fixer

At the end of this step, a unit test, including several test cases, will be generated for a PUT. The test cases are syntactically fixed.

$ python generate_test_oracle.py [prompt type] [dataset] [task_num] [script name] [output prefix name] [csv report filename (.csv)]
  • [prompt type] defines the type of initial prompt. It is normal to indicate zero-shot learning or fewshot to indicate few-shot learning.
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [task_num] is the identifier or task number within the dataset. It is within [1,163] for the HumanEval dataset, and within [1,5] for Refactory.
  • [script name] is the name of PUT.
  • [output prefix name] is the prefix to name the output or the test cases.
  • [csv report filename (.csv)] is a report on different numbers of attempts on LLMC and fixing its syntax errors. It is up to 10 attempts.

2. Functional repair

semantic_err_correction.py repairs the functional errors in assertions.

$ python semantic_err_correction.py [dataset] [task_num] [test name] [output prefix name] [csv report filename (.csv)]
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [task_num] is the identifier or task number within the dataset. It is within [1,163] for the HumanEval dataset, and within [1,5] for Refoctory.
  • [test name] is the name of the Initial Unit Test (IUT), generated in step 1.
  • [output prefix name] is the prefix to name the output or the test cases.
  • [csv report filename (.csv)] is a report on a summary of functional repair (number of assertions with functional issue, number of fixed, etc.)

3. Generate mutants

We use MutPy [cite] to generate mutants of a PUT.

$ python Mutants_generation.py [dataset] [task_num] [script name] [csv report filename (.csv)]
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [task_num] is the identifier or task number within the dataset. It is within [1,163] for the HumanEval dataset, and within [1,5] for Refoctory.
  • [script name] is the name of PUT.
  • [csv report filename (.csv)] generates reports on different types of mutants that it injected into PUT.

4. Calculate Mutation Score (MS)

After generating mutants, Mutation_Score.py calculates the MS for IUT.

$ python Mutation_Score.py [dataset] [task_num] [test name] [csv report filename (.csv)]
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [task_num] is the identifier or task number within the dataset. It is within [1,163] for HumanEval dataset, and within [1,5] for Refoctory.
  • [test name] is the name of the Initial Unit Test (IUT), generated in step 1.
  • [csv report filename (.csv)] is a report on MS and a list of surviving mutants.

5. Prompt augmentation

The surviving mutant in step 4 indicates the weaknesses of IUT'. In our prompt-based learning technique, MuTAPuses those mutants to improve the effectiveness of theIUT`. The final output is named Augmented Unit Test (AUT).

$ python augmented_prompt.py [prompt type] [dataset] [task_num] [script name] [output prefix name] [csv report filename (.csv)]
  • [prompt type] defines the type of initial prompt. It is normal to indicate zero-shot learning or fewshot to indicate few-shot learning.
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [task_num] is the identifier or task number within the dataset. It is within [1,163] for the HumanEval dataset, and within [1,5] for Refoctory.
  • [script name] is the name of PUT.
  • [output prefix name] is the prefix to name the output or the test cases.
  • [csv report filename (.csv)] is the report MS and surviving mutants, generated in step 4.

6. Merge test cases

This step merges IUT with the AUT.

python Merge_all_mut.py [dataset] [task_num] [inital test name] [augmented test name] [output name]
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [task_num] is the identifier or task number within the dataset. It is within [1,163] for the HumanEval dataset, and within [1,5] for Refoctory.
  • [inital test name] is the name of the files including initial test cases.
  • [augmented test name] is the name of the files including test cases after calling the augmented prompt on LLMC.
  • [output name] is the name of the file including final test cases.

7. Greedy minimization

This step tries to minimize the number of assertions while maximizing the MS. greedy_test_generator.py runs on all PUT

python greedy_test_generator.py [dataset] [csv report filename (.csv)]
  • [dataset] indicates the database. We have two datasets in our experiment. The first one is HumanEval and the second one is Refactory which is a benchmark for bug repairing.
  • [csv report filename (.csv)]' is a report file on applying a greedy algorithm on all PUT`s within the dataset.

Example

Here is an example of PUT from the HumanEval dataset. We used the few-shot learning technique to generate the initial input for this example.

#the PUT92
def any_int(x, y, z): 
          if isinstance(x,int) and isinstance(y,int) and isinstance(z,int):    
               if (x+y==z) or (x+z==y) or (y+z==x):           
                    return True       
               return False   
          return False
python generate_test_oracle.py "fewshot" "HumanEval" 92 "script_NDS_" "T_O_FS_synxfixed_" "fewshot_synx_fix.csv"
python semantic_err_correction.py "HumanEval" 92 "T_O_FS_synxfixed_" "T_O_FS_semticfixed_" "fewshot_semantic_fix.csv"
python Mutants_generation.py "HumanEval" 92 "script_NDS_" "mutant_type.csv"
python Mutation_Score.py "HumanEval" 92  "T_O_FS_semticfixed_" "fewshot_mutant_score.csv"
python augmented_prompt.py "fewshot" "HumanEval" 92 "T_O_FS_semticfixed_" "test_oracle_FS_Mut_" "fewshot_mutant_score.csv"
python Merge_all_mut.py  "HumanEval" 92 "T_O_FS_semticfixed_" "test_oracle_FS_Mut_" "T_O_FS_Mut_all_"
python greedy_test_generator.py "HumanEval" "greedy_FS_results.scv"

The final test case that MuTAP generates for this example PUT is as follows:

def test():
    assert any_int(3, 2, 5) == True
    assert any_int(-3, -2, 1) == True
    assert any_int(3, 2, 2) == False
    assert any_int(3, 2, 1) == True
    assert any_int(3.6, -2.2, 2) == False

Load Data

Test cases generated by both initial prompt types, before and after augmentation are stored into two jsonl files: Codex_test.jsonl.gz and llama2_test.jsonl.gz. Read_json_data.py loads all test cases into a pickle file.

Citation

Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

@article{,
  title={Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing},
  author={Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab, Foutse Khomh, Michel C. Desmarais},
  journal={https://arxiv.org/abs/2308.16557},
  year={2023}
}