---
name: Abstract State Analyzer
slug: abstract-state-analyzer
category: Quality
description: Abstract State Analyzer performs static abstract interpretation to infer possible program states, value ranges, and data properties without running code. Use it to find likely runtime errors such as out-of-bounds access, null dereferences, division by zero, type inconsistencies, and integer overflow.
github: "https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/abstract-state-analyzer"
language: Python
stars: 247
forks: 23
install: "npx degit https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/abstract-state-analyzer ~/.claude/skills/abstract-state-analyzer"
installs_to: ~/.claude/skills/abstract-state-analyzer
source_path: skills/abstract-state-analyzer/SKILL.md
collection_size: 25
category_size: 1418
collection_url: "https://dirskills.com/collections/ArabelaTso/Skills-4-SE"
added: 2026-09-02T05:21:26.553Z
last_synced: 2026-09-02T05:21:26.553Z
canonical_url: "https://dirskills.com/skills/abstract-state-analyzer"
---

# Abstract State Analyzer

Abstract State Analyzer performs static abstract interpretation to infer possible program states, value ranges, and data properties without running code. Use it to find likely runtime errors such as out-of-bounds access, null dereferences, division by zero, type inconsistencies, and integer overflow.

**Install:**

```bash
npx degit https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/abstract-state-analyzer ~/.claude/skills/abstract-state-analyzer
```

## README

# Abstract State Analyzer

## Overview

This skill performs abstract interpretation to statically analyze source code and infer possible program states, variable ranges, and data properties. It identifies potential runtime errors without executing the program.

## Analysis Workflow

### Step 1: Parse and Understand Code Structure

Analyze the code to identify:
- Functions and their control flow
- Variable declarations and types
- Loops and conditionals
- Array/buffer operations
- Pointer/reference operations
- Function calls and parameter passing

### Step 2: Select Abstract Domains

Choose appropriate abstract domains based on the analysis goals:

**Interval Domain**: Track numeric variable ranges
- Example: `x ∈ [0, 100]` means x is between 0 and 100
- Good for: Array bounds checking, overflow detection

**Sign Domain**: Track whether values are positive, negative, or zero
- Values: {+, -, 0, ⊤}
- Good for: Division by zero, sign-dependent operations

**Null Domain**: Track whether pointers/references can be null
- Values: {null, not-null, maybe-null, ⊤}
- Good for: Null dereference detection

**Type Domain**: Track possible types of variables
- Good for: Type consistency checking, dynamic language analysis

**Combination**: Use multiple domains together for more precise analysis

### Step 3: Initialize Abstract States

Set initial abstract values for:
- Function parameters (based on preconditions or ⊤ for unknown)
- Global variables
- Constants and literals

Example:
```python
def process(arr, index):
    # Initial state:
    # arr: not-null (assumed)
    # index: ⊤ (unknown integer)
```

### Step 4: Perform Forward Analysis

Propagate abstract states through the program:

**Assignment**: Update abstract value
```python
x = 5
# x: [5, 5]

y = x + 3
# y: [8, 8]
```

**Conditionals**: Split into branches
```python
if x > 10:
    # Branch 1: x ∈ [11, ∞]
else:
    # Branch 2: x ∈ [-∞, 10]
```

**Loops**: Iterate until fixpoint
```python
i = 0
while i < n:
    # Iteration 1: i ∈ [0, 0]
    # Iteration 2: i ∈ [0, 1]
    # ...
    # Fixpoint: i ∈ [0, n-1]
    i += 1
```

**Join Points**: Merge states from multiple paths
```python
if condition:
    x = 5  # x: [5, 5]
else:
    x = 10  # x: [10, 10]
# After join: x ∈ [5, 10]
```

### Step 5: Detect Potential Errors

Check for violations at each operation:

**Array Bounds**:
```python
arr[index]
# Check: index ∈ [0, len(arr)-1]?
# If index: ⊤ → Potential out-of-bounds
# If index: [0, 5] and len(arr) = 10 → Safe
```

**Null Dereference**:
```python
ptr.field
# Check: ptr is not-null?
# If ptr: maybe-null → Potential null dereference
```

**Division by Zero**:
```python
x / y
# Check: 0 ∉ y?
# If y: [1, 10] → Safe
# If y: [-5, 5] → Potential division by zero
```

**Integer Overflow**:
```python
x = a * b
# Check: a * b within type bounds?
# If a: [1000, 2000], b: [1000, 2000] → Potential overflow for int32
```

**Type Inconsistency**:
```python
result = func(arg)
# Check: arg type matches parameter type?
```

### Step 6: Report Findings

For each potential error, report:

1. **Location**: File, line number, function
2. **Error Type**: Out-of-bounds, null dereference, etc.
3. **Abstract State**: Variable values at the error point
4. **Severity**: Definite error vs. potential error
5. **Explanation**: Why the error might occur
6. **Suggestion**: How to fix (add check, change bounds, etc.)

## Complete Example

```python
def find_max(arr, n):
    if n <= 0:
        return None

    max_val = arr[0]
    i = 1
    while i < n:
        if arr[i] > max_val:
            max_val = arr[i]
        i += 1
    return max_val
```

**Analysis:**

**Initial State:**
- arr: not-null (assumed)
- n: ⊤ (unknown integer)

**Line 2: `if n <= 0`**
- Branch 1 (n ≤ 0): n ∈ [-∞, 0]
- Branch 2 (n > 0): n ∈ [1, ∞]

**Line 3: `return None`** (Branch 1)
- Safe return

**Line 5: `max_val = arr[0]`** (Branch 2)
- Access: arr[0]
- Check: 0 < len(arr)?
- **POTENTIAL ERROR**: arr length unknown, might be empty
- State: max_val = arr[0], n ∈ [1, ∞]

**Line 6: `i = 1`**
- State: i = [1, 1]

**Line 7: `while i < n`**
- Loop invariant: i ∈ [1, n]
- Fixpoint: i ∈ [1, n-1] inside loop

**Line 8: `if arr[i] > max_val`**
- Access: arr[i] where i ∈ [1, n-1]
- Check: i < len(arr)?
- **POTENTIAL ERROR**: If n > len(arr), out-of-bounds access
- State: max_val updated if arr[i] > max_val

**Line 10: `i += 1`**
- State: i ∈ [2, n]

**Report:**

```
POTENTIAL ERRORS FOUND:

1. Out-of-Bounds Access
   Location: line 5, arr[0]
   State: n ∈ [1, ∞], arr length unknown
   Severity: Potential
   Explanation: Array 'arr' might be empty when n > 0
   Suggestion: Add check: if len(arr) == 0 or add precondition

2. Out-of-Bounds Access
   Location: line 8, arr[i]
   State: i ∈ [1, n-1], arr length unknown
   Severity: Potential
   Explanation: If n > len(arr), accessing beyond array bounds
   Suggestion: Add precondition: n <= len(arr) or check i < len(arr)
```

## Language-Specific Considerations

### C/C++
- Track pointer arithmetic carefully
- Consider undefined behavior (signed overflow, null dereference)
- Analyze memory allocation/deallocation
- Check buffer sizes for string operations

### Python
- Dynamic typing requires type domain
- List/dict operations need bounds checking
- None values require null domain
- Consider duck typing and attribute access

### Java
- Null pointer exceptions
- Array bounds (ArrayIndexOutOfBoundsException)
- Integer overflow (silent wraparound)
- Type casting (ClassCastException)

### JavaScript
- Undefined and null values
- Type coercion issues
- Array bounds (returns undefined, not error)
- Property access on null/undefined

## Handling Complexity

### Widening for Loops
When loops don't converge quickly, apply widening:
```python
# Instead of: [0, 0] → [0, 1] → [0, 2] → ...
# Widen to: [0, ∞]
```

### Function Summaries
For called functions, use summaries instead of full analysis:
```python
def helper(x):
    # Summary: returns x + 1, no errors
    return x + 1

# Use summary instead of analyzing helper body
```

### Path Sensitivity
For complex conditionals, track path conditions:
```python
if x > 0 and x < 10:
    # x ∈ [1, 9] (path-sensitive)
    # vs x ∈ [-∞, ∞] (path-insensitive)
```

## References

For detailed information on abstract interpretation techniques and domains:
- **references/abstract_domains.md**: Detailed abstract domain definitions and operations
- **references/analysis_patterns.md**: Common analysis patterns for different error types
- **references/language_specifics.md**: Language-specific analysis considerations