fandango-learn 0.0.2

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fandango-learn

This repository contains the code for the Fandango Learn project. The goal is to automatically learn fandango constraints form a set of inputs.

Overall, the learner will be integrated into Avicenna, which provides the necessary infrastructure of the hypothesis refinement loop. Furthermore, Avicenna provides the means to automatically learn the set of relevant non-terminals, reducing the search space for the learner.

Table of Contents

• Quick Start (Prototype)
• Installation
• Notebooks
• Reusability
• Benchmark-Results

---

Quick Start (Prototype)

Introduction to FandangoLearner

This notebook demonstrates how to use FandangoLearner, a pattern based approach that automatically learns constraints to explain why a program fails.

The core idea of FandangoLearner is to identify patterns in inputs that lead to program errors or unexpected behaviors. Using these patterns, it generates constraints in the Fandango language to help developers understand input-related bugs.

Step 1: Define the Grammar

We start by defining the grammar for our input language. This example focuses on arithmetic expressions using trigonometric and square root functions.

from fdlearn.interface.fandango import parse_contents

grammar = """
<start> ::= <arithexp>;
<arithexp> ::= <function>"("<number>")";
<function> ::= "sqrt" | "cos" | "sin" | "tan";
<number> ::= <maybeminus><onenine><maybedigits> | "0";
<maybeminus> ::= "-" | "";
<onenine> ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9";
<maybedigits> ::= <digit>*;
<digit>::=  "0" | <onenine>;
"""

grammar, _ = parse_contents(grammar)

Step 2: Provide Initial Inputs

We supply a set of example inputs along with their expected outcomes (True for failure, False otherwise).

initial_inputs = {
    ("sqrt(-900)", True),  # This input causes a failure.
    ("sqrt(-10)", True),   # Another failure case.
    ("sqrt(0)", False),    # This input works correctly.
    ("sin(-900)", False),  # Works correctly.
    ("sqrt(2)", False),    # Works correctly.
    ("cos(10)", False),    # Works correctly.
}

Convert inputs to FandangoInput objects

from fdlearn.learner import FandangoInput

initial_inputs = {
  FandangoInput.from_str(grammar, inp, oracle)
  for inp, oracle in initial_inputs
}

Step 3: Select Relevant Non-Terminals (Optional)

We specify the non-terminals in the grammar that are likely related to the program's failure behavior. This step is optional but can help focus the learning process on specific parts of the grammar. Later, we will see that Avicenna can automatically learn relevant non-terminals.

from fdlearn.learner import NonTerminal

relevant_non_terminals = {
  NonTerminal("<number>"),
  NonTerminal("<maybeminus>"),
  NonTerminal("<function>"),
}

Step 4: Learn Constraints

Using the FandangoLearner, we learn constraints that explain why certain inputs fail.

from fdlearn.learner import FandangoLearner

learner = FandangoLearner(grammar)

learned_constraints = learner.learn_constraints(
  initial_inputs,
  relevant_non_terminals=relevant_non_terminals
)

Step 5: Analyze Results

Finally, we analyze the constraints generated by FandangoLearner to understand the root cause of failures.

print("Learned Constraints:")
for constraint in learner.get_best_candidates():
    print(constraint)

The output will show the constraints that best explain the failures in the initial inputs.

(int(<number>) <= -10 and str(<function>) == 'sqrt'), 
Precision: 1.0, Recall: 1.0 (based on 2 failing and 4 passing inputs)

We can see that the constraint (int(<number>) <= -10 and str(<function>) == 'sqrt') explains why the inputs sqrt(-900) and sqrt(-10) fail. However, this constraint is too specific and does not generalize well to other inputs. Thus, we need a feedback loop that automatically refines these constraints to generate general constraints that captures the essence of the failure. We will use Avicenna to provide this feedback loop.

---

Install, Development, Testing

Installation

To use FandangoLearn, you need to install both FandangoFuzzer (from its learner branch) and FandangoLearn. We recommend using a dedicated virtual environment:

python3.12 -m venv venv
source venv/bin/activate
pip install --upgrade pip

Now, install FandangoFuzzer from the learner branch followed by FandangoLearn:

pip install git+https://github.com/fandango-fuzzer/fandango.git@learner
pip install fandangolearn

Development

If you plan to extend or evaluate FandangoLearn locally, clone this repository:

git clone https://github.com/fandango-fuzzer/fandango-learn.git
cd fandango-learn

Create and activate a virtual environment (recommended):

python3.12 -m venv venv
source venv/bin/activate

pip install --upgrade pip
pip install -r requirements.txt
pip install -e .

Testing

To install the test dependencies and run the tests:

make install-tests
make tests

That’s it! You’re now set up to use, develop, and test FandangoLearn. If you encounter any issues or have suggestions, feel free to open an issue or submit a pull request.

Notebooks

Work in progress.

This repository contains jupyter notebooks that demonstrate how to use the FandangoLearner to learn constraints from a set of inputs. Furthermore, the notebooks show how FandangoLearner works and how it can be, for instance, integrated into Avicenna.

Current notebooks:

• Introduction to FandangoLearner: Demonstrates how to use the FandangoLearner to learn constraints from a set of inputs.
• Automated Refinement: Shows how we can automatically refine constraints learned by FandangoLearner.
• Adding Patterns: Demonstrates how to add new patterns to the FandangoLearner to improve the learning process.

More notebooks will be added soon.

---

Development Notes

Reusability

The code should be reusable for other projects, such as Avicenna. Therefore, the code uses many abstract classes that are already implemented in Avicenna and AvicennaISLearn. This makes comparing both approaches FandangoLearn and ISLearn extremely easy.

Benchmark

Date: 2025-01-27 10:45:40
Git Branch: dev
Last Commit: 76d77e252f2dcc848e705e62ab7fc56485b008f6

Subject	Total	Correct	Percentage	#Seeds	Mean Precision	P-StdDev	Mean Recall	R-StdDev	Time (s)
Calculator	1	1	100.00	5	1.0000	0.0000	0.9174	0.0000	0.0326
CalculatorRE	1	1	100.00	5	1.0000	0.0000	1.0000	0.0000	3.2854
Heartbleed	4	2	50.00	5	1.0000	0.0000	1.0000	0.0000	0.0121
HeartbleedRE	2	2	100.00	5	1.0000	0.0000	1.0000	0.0000	4.1509
Middle	84	1	1.19	5	1.0000	0.0000	1.0000	0.0000	0.0395
MiddleRE	1	1	55.56	5	1.0000	0.0000	1.0000	0.0000	32.1872
Pysnooper2	2	1	50.00	5	1.0000	0.0000	1.0000	0.0000	0.0492
Pysnooper3	2	2	100.00	5	1.0000	0.0000	1.0000	0.0000	0.0592

First Ideas

• Use Pattern Based Approach
  • Will likely lead to combinatorial explosion
  • Requires similar ideas as scatched in the Avicenna paper, i.e. reduce the number of relevant non-terminals
• Build atomic constraints
  • Use the atomic constraints to build more complex constraints
  • Atomic constraints will be combined to more complex constraints with conjunctions and disjunctions.
• Implement different filter mechanisms
  • Allow to rank constraints based on different criteria like precision, recall, etc.

new idea:

• exists and forall constraints can be quite similar to each other, when that use bounded nonterminals that are applied only once.
  • we might want to use forall constraints only when they are really needed, i.e. when the nonterminal is used multiple times.

TODOs

• Only add new constraints if they are better than the best one so far.
• InputReducer
• AttributeSearches
• Better Negation
• CLI
• Documentation
• Extend Eval