asterrdetection 0.7

A package that identifies semantic errors in faulty code by comparing it against a set of expected correct code versions.

AST ERROR DETECTION

Overview

ast-error-detection is a Python library designed for analyzing and annotating algorithmic errors in code. It leverages the Abstract Syntax Tree (AST) module from Python's standard library to identify, categorize, and contextualize errors. The output of the library is a list of errors, where each error is a dictionary describing the issue and its context.

This library is under an ongoing scientific study. If you use it for academic purposes, please cite the forthcoming publication (details will be provided).

---

Features

• Parses and processes Python code using the AST module.
• Identifies and categorizes errors with detailed contextual information.
• Provides actionable insights for debugging.

---

Installation

Install the package via pip:

pip install ast_error_detection

---

Usage

There are 2 ways to use this library.

Primary Error Detection

The Primary Error Detection layer is responsible for identifying low-level structural and content modifications between two trees (typically a reference and a hypothesis tree). It serves as the foundational layer for subsequent high-level error interpretation. This component relies on the Zhang-Shasha Edit Distance Algorithm, a classic algorithm for computing the minimum-cost sequence of operations needed to transform one tree into another. The algorithm outputs three types of edit operations:

• Insertions
• Deletions
• Updates

Each operation corresponds to a specific node-level change and includes metadata context. These operations collectively represent the raw set of changes (or "primary errors") from which higher-level interpretations can be derived.

from ast-error-detection import get_primary_code_errors

# Example erroneous code to analyze
code_1 = """ 
# Code snippet here
"""
# Example expected code
code_2 = """ 
# Code snippet here
"""

# Convert AST to custom node representation
result = get_primary_code_errors(code_1, code_2)

# Print the results
print(result)

Output Format

The output is always a list of errors. Each error is a dictionary structured as follows:

• For delete or insert errors (3 elements):

  • error: Description of the error.
  • value: The value to delete or insert.
  • context: Contextual location of the error (see below).

• For update errors (4 elements):

  • error: Description of the error.
  • old_value: The old value to update.
  • new_value: The new value to update.
  • context: Contextual location of the error.

Context Format

The context describes where the error occurred in the code execution hierarchy. For example:

Module > Function Name > For Loop > If > Condition

This indicates that the error is in the condition of an if statement inside a for loop within a function in the module.

Typology Based Error Detection

While primary error detection provides atomic operations, Typology-Based Error Detection serves as a semantic interpretation layer. It classifies the primary operations into predefined error types (typologies), allowing for more meaningful feedback and error analysis.

This module:

• Consumes the output of the Primary Error Detection layer.
• Applies a mapping schema to group and classify operations into specific typology classes.
• Detects compound or context-dependent errors by evaluating relationships between multiple primary operations. (logic of each error is defined in the Table below)

Unlike simple pairwise comparison, this component supports comparison of one erroneous code instance against multiple possible correct codes.

• The first input is the hypothesis (erroneous AST).
• The second input is a list of reference ASTs (multiple correct solutions).
• The system computes the edit distance to each reference, selects the closest match, and performs typology-based annotation against that reference.

This ensures the most contextually relevant and minimal-error interpretation is selected for annotation.

from ast-error-detection import get_typology_based_code_error

# Example erroneous code to analyze
code_1 = """ 
# Code snippet here
"""
# Example expected code list
expected_codes = [
  """ 
  # Code snippet 1
  """,
  """ 
  # Code snippet 2
  """
]

# Convert AST to custom node representation
result = get_typology_based_code_error(code_1, expected_codes)

# Print the results
print(result)

Output Format

A list of string (Error tags) from the predefined set of Error tags (Ref Table below)

---

Example

Input Code 1

print('Hello')

Input Code 2

print('Hello1')

Output with Primary error Detection

[('CONST_VALUE_MISMATCH', "Const: 'Hello'", "Const: 'Hello1'", "Module > Call: print > Const: 'Hello'")]

Output with Typology error Detection

['FUNCTION_CALL_PARAMETER_ERROR']

List of error tags

ERROR	DETAILS / EXAMPLES	TASK TYPE(S)	ERROR TAG	IN LIBRARY?
	VARIABLES
(VA-Err1) Error in variable declaration and initialization	Wrong type chosen for the variable or wrong value during initialization	VA – Produce a variable (and children)	VARIABLE_DECLARATION_INITIALIZATION_ERROR	✗
(VA-Err2) Missing variable increment	Typical counter problem: variable never modified or reset each time	—	VARIABLE_MISSING_INCREMENT	✗
(VA-Err3) Invalid variable name	Forbidden character or reserved keyword used	VA – Choose a valid name	VARIABLE_INVALID_NAME	✗
	CONDITIONAL STATEMENTS
(CS-Err1) Incorrect number of branches	A case is missing or branches overlap	IC – Determine the necessary branches; partition the cases	CONDITIONAL_MISSING_BRANCH	✗
(CS-Err2) Misplaced instructions in a conditional	An instruction is in the wrong branch or outside the conditional	IC – Assign the right action to each branch	CONDITIONAL_MISPLACED_INSTRUCTIONS	✗
	FUNCTIONS
(F-Err1) Function definition error	Missing/incorrect parameters, wrong preconditions, incorrect return	F – Define parameters, preconditions, returns	FUNCTION_DEFINITION_ERROR	✗
(F-Err2) Function call parameter error	Called with wrong parameters or return value not captured	—	FUNCTION_CALL_PARAMETER_ERROR	✗
(F-Err3) Invalid function or parameter name	Forbidden character or reserved keyword used	F – Name function and parameters	FUNCTION_INVALID_NAME	✗
	LOOPS
(LO-Err1) Loop iterator usage error	Wrong start/end values or iterator ignored	B – Correct use of loop iterator	LOOP_ITERATOR_USAGE_ERROR	✗
(LO-Err2) Off-by-one loop error	Loop runs one time too many or too few	B – Determine iteration count	LOOP_OFF_BY_ONE_ERROR	✗
(LO-Err3) Wrong loop iteration count (> 2)	Loop executes an entirely wrong number of times	B – Determine iteration count	LOOP_WRONG_ITERATION_COUNT	✗
(LO-Err4) Start condition error	Incorrect initial loop condition	B – Stop condition (unbounded loop)	LOOP_START_CONDITION_ERROR	✗
(LO-Err5) Update condition error	Stop condition never updated or updated incorrectly	B – Modify stop condition	LOOP_UPDATE_CONDITION_ERROR	✗
(LO-Err6) Missing instruction in loop body (not present anywhere)	Expected instruction absent from both loop and program	B – Instructions each iteration	LOOP_BODY_MISSING_INSTRUCTIONS	✗
(LO-Err7) Missing instruction in loop body (moved elsewhere)	Instruction exists but outside the loop	B – Instructions each iteration	LOOP_BODY_INSTRUCTIONS_MOVED_OUT	✗
(LO-Err8) Incorrect instruction close to expected	Instruction present but incorrect / nearly correct	B – Instructions each iteration	LOOP_BODY_INCORRECT_INSTRUCTIONS_NEAR	✗
(LO-Err9) Extra unwanted instruction in loop body	Superfluous instruction unrelated to task	B – Instructions each iteration	LOOP_BODY_EXTRA_INSTRUCTIONS	✗
	EXPRESSIONS
(EXP-Err1) Boolean condition error	Uses < instead of ≤, etc.	IC – Correct boolean expression	EXPRESSION_BOOLEAN_CONDITION_ERROR	✗
(EXP-Err2) Assignment expression error	Part of expression missing or wrong operator	VA – Assign correct expression	EXPRESSION_ASSIGNMENT_ERROR	✗
	PROGRAM & ALGORITHM
(PA-Err1) Problem decomposition / strategy error	Program omits expected control structures	P/A – Design & decompose algorithm	PROGRAM_DECOMPOSITION_STRATEGY_ERROR	✗
(PA-Err2) Requirements misunderstood	Program correct but solves a different task	—	PROGRAM_REQUIREMENTS_MISUNDERSTOOD	✗
(PA-Err3) Incomplete program	Correct fragments present but sequence missing	A – Complete instructions	PROGRAM_INCOMPLETE	✗
(PA-Err4) Program not optimized	Task done but redundantly (no loops/functions)	P/A – Optimize or adapt algorithm	PROGRAM_NOT_OPTIMIZED	✗

These error tags were created as part of an ongoing research project that systematically analyzes novice programming mistakes in a controlled study. The formal paper detailing the methodology and validation of the taxonomy is still in preparation and has not yet been published.

---

License

This project is licensed under the GNU Affero General Public License v3 (AGPL-3.0). If you wish to use this library for proprietary or commercial purposes, you must obtain a separate license.

Please contact Badmavasan at [[e-mail]] for commercial licensing inquiries.

---

Scientific Publication

This library is part of an ongoing scientific study. If you use it for academic purposes, please cite the forthcoming publication:

[Publication details will be added here once available.]

Stay tuned for updates!