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Implement a Package Dependency Resolver

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Task Overview

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Task Creator Model

Answering Models

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Task Prompt

Write a Python function `resolve(requirements, package_index)` that implements a dependency resolution algorithm. The function should take two arguments: 1. `requirements`: A list of strings, where each string is an initial package requirement (e.g., `["A>=1.2.0", "B"]`). 2. `package_index`: A dictionary representing all available packages. The keys are package names. The values are dictionaries where keys are version strings (e.g., '1.2.3') and values are lists of dependency requirement strings for that version....

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Write a Python function `resolve(requirements, package_index)` that implements a dependency resolution algorithm. The function should take two arguments: 1. `requirements`: A list of strings, where each string is an initial package requirement (e.g., `["A>=1.2.0", "B"]`). 2. `package_index`: A dictionary representing all available packages. The keys are package names. The values are dictionaries where keys are version strings (e.g., '1.2.3') and values are lists of dependency requirement strings for that version. Your function should return a dictionary mapping each required package name (including transitive dependencies) to a single, resolved version string that satisfies all constraints. This is often called a 'lock file'. Your algorithm must be able to handle transitive dependencies and version conflicts. If a valid set of packages cannot be found, the function should raise a `ValueError` with a clear message explaining the conflict. For simplicity, you can assume: - Versions follow semantic versioning (e.g., '1.2.3'). - Requirement specifiers are one of: `==`, `!=`, `>=`, `<=`, `>`, `<`. A requirement without a specifier (e.g., "B") implies any version is acceptable. - Your solution should aim to select the latest possible version of each package that satisfies all constraints.

Task Context

In software development, package managers (like pip, npm, or cargo) are essential tools. A core component of any package manager is the dependency resolver. Its job is to find a set of compatible package versions that satisfy the project's direct and indirect (transitive) dependencies. This is a complex constraint satisfaction problem. A naive approach can easily lead to incorrect results or infinite loops (in the case of circular dependencies). Here is an example of a `package_index`: ```python { "A": {...

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In software development, package managers (like pip, npm, or cargo) are essential tools. A core component of any package manager is the dependency resolver. Its job is to find a set of compatible package versions that satisfy the project's direct and indirect (transitive) dependencies. This is a complex constraint satisfaction problem. A naive approach can easily lead to incorrect results or infinite loops (in the case of circular dependencies). Here is an example of a `package_index`: ```python { "A": { "1.0.0": ["B<2.0.0"], "1.1.0": ["B<3.0.0"], "1.2.0": ["C==1.0.0"] }, "B": { "1.5.0": [], "2.5.0": [] }, "C": { "1.0.0": ["B>=2.0.0"], "1.1.0": [] } } ``` For example, if the initial `requirements` are `["A>=1.1.0"]`, a conflict exists: `A==1.1.0` needs `B<3.0.0`, while `A==1.2.0` needs `C==1.0.0`, which in turn needs `B>=2.0.0`. If we pick `A==1.2.0`, we need `C==1.0.0`, which needs `B>=2.0.0`. The only version of B that satisfies this is `B==2.5.0`. This set (`A=1.2.0`, `C=1.0.0`, `B=2.5.0`) is a valid resolution. Your function should find it.

Judging Policy

A good answer must provide a correct, efficient, and robust implementation of the dependency resolver. 1. **Correctness:** The primary criterion is whether the function returns a valid and complete set of dependencies that satisfies all constraints for solvable inputs. It must correctly identify unsolvable inputs by raising an appropriate error. The solution should prioritize selecting the latest valid versions of packages. 2. **Algorithm and Efficiency:** The solution should use a sound algorithm, such as a bac...

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A good answer must provide a correct, efficient, and robust implementation of the dependency resolver. 1. **Correctness:** The primary criterion is whether the function returns a valid and complete set of dependencies that satisfies all constraints for solvable inputs. It must correctly identify unsolvable inputs by raising an appropriate error. The solution should prioritize selecting the latest valid versions of packages. 2. **Algorithm and Efficiency:** The solution should use a sound algorithm, such as a backtracking search with memoization or a graph-based approach, to explore the dependency tree. A naive, inefficient brute-force solution that would perform poorly on moderately complex graphs is considered low-quality. 3. **Error Handling:** For unsolvable requirements, the `ValueError` message must be clear and helpful, ideally identifying the specific packages and version constraints that are in conflict. 4. **Code Quality and Design:** The code should be well-structured, readable, and use appropriate data structures. The logic for parsing version strings and requirement specifiers should be robust and handle all specified operators correctly. 5. **Edge Cases:** The solution must gracefully handle edge cases, including circular dependencies (without getting into an infinite loop), requests for packages that do not exist, and empty inputs.

Answers

def resolve(requirements, package_index): """ Resolve package versions given initial requirements and a package index. requirements: list of requirement strings like "A>=1.2.0" or "B" package_index: dict of package -> { version_str: [dependency requirement strings] } Returns: dict mapping package -> resolved version string Raises ValueError on conflict with explanation. """ import copy import re OPS = ["==", "!=", ">=", "<=", ">", "<"] def parse_version(v): # Parse semantic version into tuple of ints (major, minor, patch, ...) parts = tuple(int(p) fo...

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def resolve(requirements, package_index): """ Resolve package versions given initial requirements and a package index. requirements: list of requirement strings like "A>=1.2.0" or "B" package_index: dict of package -> { version_str: [dependency requirement strings] } Returns: dict mapping package -> resolved version string Raises ValueError on conflict with explanation. """ import copy import re OPS = ["==", "!=", ">=", "<=", ">", "<"] def parse_version(v): # Parse semantic version into tuple of ints (major, minor, patch, ...) parts = tuple(int(p) for p in v.split('.')) return parts def version_cmp_key(v): return parse_version(v) def parse_requirement(req): req = req.strip() # Find operator if present for op in OPS: if op in req: name, ver = req.split(op, 1) name = name.strip() ver = ver.strip() return name, op, ver # No operator means any version name = req return name.strip(), None, None def satisfies_one(ver_str, op, ver_req_str): if op is None: return True left = parse_version(ver_str) right = parse_version(ver_req_str) if op == '==': return left == right if op == '!=': return left != right if op == '>=': return left >= right if op == '<=': return left <= right if op == '>': return left > right if op == '<': return left < right return False def satisfies_all(ver_str, constraints): # constraints: list of (op, ver_str) where op may be None return all(satisfies_one(ver_str, op, vreq) for (op, vreq) in constraints) # Build initial constraints dict: package -> list of (op, ver) constraints = {} def add_constraint(constraints_map, pkg, op, ver): if pkg not in constraints_map: constraints_map[pkg] = [] # Avoid duplicate identical constraints if (op, ver) not in constraints_map[pkg]: constraints_map[pkg].append((op, ver)) # Initialize from top-level requirements for req in requirements: name, op, ver = parse_requirement(req) if name not in package_index: raise ValueError(f"Package '{name}' required but not found in package index") add_constraint(constraints, name, op, ver) # Helper to compute allowed versions list for a package under its constraints def allowed_versions(pkg, constraints_map): if pkg not in package_index: raise ValueError(f"Package '{pkg}' not found in package index") cons = constraints_map.get(pkg, []) versions = list(package_index[pkg].keys()) # Filter filtered = [v for v in versions if satisfies_all(v, cons)] # Sort descending by version (latest first) filtered.sort(key=version_cmp_key, reverse=True) return filtered # Backtracking search def search(resolved, constraints_map): # If every package with constraints is resolved, we are done to_resolve = [p for p in constraints_map.keys() if p not in resolved] if not to_resolve: return resolved # Choose the package with fewest candidates to minimize branching candidate_info = [] # list of (pkg, candidates) for pkg in to_resolve: try: cand = allowed_versions(pkg, constraints_map) except ValueError as e: # Package missing raise if not cand: # Conflict: no versions satisfy constraints for this package # Build readable constraint string cons = constraints_map.get(pkg, []) cons_str = ", ".join([(op + v) if op else "any" for (op, v) in cons]) raise ValueError(f"No versions of package '{pkg}' satisfy constraints: {cons_str}") candidate_info.append((pkg, cand)) # sort by number of candidates ascending candidate_info.sort(key=lambda x: len(x[1])) pkg, candidates = candidate_info[0] # Try each candidate (latest-first already) for ver in candidates: new_resolved = dict(resolved) new_resolved[pkg] = ver # Clone constraints for next level new_constraints = copy.deepcopy(constraints_map) # When we set pkg=ver, we must ensure existing constraints for pkg are satisfied if not satisfies_all(ver, new_constraints.get(pkg, [])): # Shouldn't happen because candidates were filtered, but keep check continue # Add its dependencies into constraints deps = package_index[pkg].get(ver, []) conflict = False conflict_msg = None for dep_req in deps: dep_name, dep_op, dep_ver = parse_requirement(dep_req) if dep_name not in package_index: conflict = True conflict_msg = f"'{pkg}=={ver}' requires missing package '{dep_name}'" break add_constraint(new_constraints, dep_name, dep_op, dep_ver) # If dep already resolved, ensure consistent if dep_name in new_resolved: assigned = new_resolved[dep_name] if not satisfies_all(assigned, new_constraints.get(dep_name, [])): conflict = True conflict_msg = (f"Conflict: {pkg}=={ver} requires {dep_req}, " f"which is incompatible with already resolved {dep_name}=={assigned}") break if conflict: # try next candidate continue # Also, it's possible that adding these constraints makes some other unresolved package have zero candidates # We'll detect that when recursion attempts to compute allowed_versions and will raise accordingly. try: result = search(new_resolved, new_constraints) return result except ValueError as e: # try next candidate last_err = e continue # If all candidates fail, raise conflict. Try to provide informative message. # Build constraint description for pkg cons = constraints_map.get(pkg, []) cons_str = ", ".join([(op + v) if op else "any" for (op, v) in cons]) raise ValueError(f"Unable to resolve package '{pkg}' with constraints: {cons_str}") # Kick off search resolved = {} result = search(resolved, constraints) return result # Example usage (uncomment to run quick test): # if __name__ == '__main__': # index = { # "A": { # "1.0.0": ["B<2.0.0"], # "1.1.0": ["B<3.0.0"], # "1.2.0": ["C==1.0.0"] # }, # "B": { # "1.5.0": [], # "2.5.0": [] # }, # "C": { # "1.0.0": ["B>=2.0.0"], # "1.1.0": [] # } # } # print(resolve(["A>=1.1.0"], index))

Result

#1 | Winner

Winning Votes

2 / 3

Average Score

76
Judge Models OpenAI GPT-5.2

Total Score

74

Overall Comments

Implements a solid backtracking dependency resolver that accumulates constraints per package, selects latest satisfying versions, and uses a smallest-domain heuristic to reduce branching. Handles transitive dependencies, missing packages, and many conflicts with reasonably clear messages. Main weaknesses are somewhat simplistic requirement parsing (operator detection via substring search could misparse unusual names) and no explicit cycle handling/memoization (though it still terminates because it resolves each package once).

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Correctness

Weight 35%
78

Correctly resolves transitive dependencies via backtracking while accumulating all constraints per package, and prefers latest versions by sorting candidates descending. Generally detects unsatisfiable constraints. Minor risks: simplistic requirement parsing and no explicit pruning/memoization.

Completeness

Weight 20%
72

Covers specified operators, transitive deps, missing packages, and empty requirements (returns {}). Does not implement advanced features like memoization or explicit cycle detection, but still avoids infinite loops by resolving each package once.

Code Quality

Weight 20%
68

Reasonably readable structure with helper functions and heuristic choice of next package. Some unnecessary imports (re unused) and deep-copy usage may be heavy; error message construction has minor rough edges.

Practical Value

Weight 15%
70

More practically usable because it handles evolving constraints and can find solutions in cases that require backtracking across earlier package choices. Still limited versus production resolvers (no memoization, simplistic parsing).

Instruction Following

Weight 10%
82

Matches the requested function signature, returns a lockfile dict, prefers latest versions, and raises ValueError on conflicts with reasonably clear messages.

Total Score

81

Overall Comments

Answer A provides a correct and well-structured solution using a backtracking algorithm. It correctly parses requirements, handles version comparisons, and prioritizes the latest versions. Error handling for conflicts and missing packages is present. However, the use of `copy.deepcopy` for the `constraints_map` in each recursive call can lead to significant performance overhead for complex dependency graphs, potentially limiting its practical scalability. The `parse_requirement` function is also slightly less robust than a regex-based approach.

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Correctness

Weight 35%
85

The core logic for resolving dependencies, handling version comparisons, and identifying conflicts is correct. It correctly prioritizes the latest versions. The `parse_requirement` method is functional but less robust than a regex-based approach.

Completeness

Weight 20%
85

The solution handles all specified requirements, including transitive dependencies, version conflicts, and missing packages. It correctly raises `ValueError` for unsolvable scenarios. The `deepcopy` mechanism, while correct, might limit its completeness for extremely large and deep dependency graphs due to performance.

Code Quality

Weight 20%
75

The code is generally well-structured with clear helper functions and comments. However, the use of `copy.deepcopy` for state management is a significant design choice that impacts efficiency. The `parse_requirement` could be more elegant.

Practical Value

Weight 15%
70

The solution provides a functional dependency resolver. However, the performance implications of `deepcopy` could make it less practical for real-world scenarios with very large or complex dependency trees, where efficiency is crucial.

Instruction Following

Weight 10%
85

All instructions are followed, including the function signature, return type, error handling, and handling of various specifiers. The requirement to aim for an efficient algorithm is met, but the `deepcopy` could be seen as a slight deviation from optimal efficiency for complex cases.

Total Score

73

Overall Comments

Answer A provides a solid backtracking dependency resolver with good structure. It uses constraint propagation by maintaining a constraints map, implements MRV (minimum remaining values) heuristic for variable ordering which improves efficiency, handles missing packages, and provides informative error messages. The code uses deepcopy for constraint maps which is correct but slightly expensive. It handles circular dependencies implicitly through the resolved set. The code is well-documented with comments and has a clean separation of concerns. One minor issue is that it doesn't explicitly handle circular dependencies with a visited set, but the resolved dict prevents infinite loops in practice since a package once resolved won't be re-resolved.

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Correctness

Weight 35%
75

Answer A correctly resolves the given example (A>=1.1.0 -> A=1.2.0, C=1.0.0, B=2.5.0) and handles conflicts by backtracking. The constraint accumulation approach ensures all constraints are checked when selecting versions. It correctly raises ValueError for unsolvable cases and missing packages.

Completeness

Weight 20%
70

Answer A handles missing packages, version conflicts, transitive dependencies, and empty inputs. It doesn't explicitly handle circular dependencies with a visited set but avoids infinite loops through the resolved dict. It covers all specified operators.

Code Quality

Weight 20%
75

Answer A has clean separation of concerns with well-named helper functions. The constraint map approach is elegant and the MRV heuristic shows algorithmic sophistication. Comments are helpful. The use of deepcopy is correct but could be optimized. The code is well-structured overall.

Practical Value

Weight 15%
70

Answer A's MRV heuristic and constraint propagation approach would perform better on real-world dependency graphs with many packages. The constraint accumulation model is closer to how real dependency resolvers work. The deepcopy overhead could be a concern for very large dependency trees.

Instruction Following

Weight 10%
75

Answer A follows all instructions: returns a dict mapping package names to versions, raises ValueError with clear messages, handles all specified operators, aims for latest versions (sorts descending), and handles transitive dependencies.

import re from functools import cmp_to_key def parse_requirement(req_str): """ Parse a requirement string like 'A>=1.2.0', 'B', 'C==1.0.0' Returns (name, operator, version) where operator and version may be None. """ req_str = req_str.strip() match = re.match(r'^([A-Za-z0-9_\-\.]+)\s*(==|!=|>=|<=|>|<)\s*([0-9][A-Za-z0-9\.\-]*)$', req_str) if match: return match.group(1), match.group(2), match.group(3) # No specifier match2 = re.match(r'^([A-Za-z0-9_\-\.]+)$', req_str) if match2: return match2.group(1), None, None raise ValueError(f"Cannot parse require...

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import re from functools import cmp_to_key def parse_requirement(req_str): """ Parse a requirement string like 'A>=1.2.0', 'B', 'C==1.0.0' Returns (name, operator, version) where operator and version may be None. """ req_str = req_str.strip() match = re.match(r'^([A-Za-z0-9_\-\.]+)\s*(==|!=|>=|<=|>|<)\s*([0-9][A-Za-z0-9\.\-]*)$', req_str) if match: return match.group(1), match.group(2), match.group(3) # No specifier match2 = re.match(r'^([A-Za-z0-9_\-\.]+)$', req_str) if match2: return match2.group(1), None, None raise ValueError(f"Cannot parse requirement: {req_str!r}") def parse_version(version_str): """Parse a version string into a tuple of integers for comparison.""" parts = [] for part in version_str.split('.'): try: parts.append(int(part)) except ValueError: parts.append(part) return tuple(parts) def version_satisfies(version_str, operator, constraint_version_str): """Check if version_str satisfies the constraint operator+constraint_version_str.""" if operator is None: return True v = parse_version(version_str) c = parse_version(constraint_version_str) if operator == '==': return v == c elif operator == '!=': return v != c elif operator == '>=': return v >= c elif operator == '<=': return v <= c elif operator == '>': return v > c elif operator == '<': return v < c return False def get_sorted_versions(package_name, package_index): """Return versions of a package sorted from latest to oldest.""" if package_name not in package_index: return [] versions = list(package_index[package_name].keys()) versions.sort(key=lambda v: parse_version(v), reverse=True) return versions def resolve(requirements, package_index): """ Resolve package dependencies. Args: requirements: List of requirement strings (e.g., ["A>=1.2.0", "B"]) package_index: Dict mapping package names to dicts of version->dependencies Returns: Dict mapping package names to resolved version strings. Raises: ValueError: If no valid resolution exists. """ # We'll use a backtracking approach. # State: a dict of {package_name: resolved_version} # We maintain a queue of requirements to process. def backtrack(resolved, pending_requirements, visited_states=None): """ resolved: dict of {pkg_name: version_str} - already resolved packages pending_requirements: list of (name, operator, version) tuples to satisfy Returns resolved dict or raises ValueError. """ if visited_states is None: visited_states = set() # Deduplicate and check pending requirements if not pending_requirements: return resolved # Take the first pending requirement name, op, ver = pending_requirements[0] rest = pending_requirements[1:] # Check if already resolved if name in resolved: # Verify the resolved version satisfies this constraint if version_satisfies(resolved[name], op, ver): return backtrack(resolved, rest, visited_states) else: # Conflict with already resolved version raise ValueError( f"Conflict: {name}=={resolved[name]} does not satisfy {name}{op or ''}{ver or ''}" ) # Package not yet resolved - try versions from latest to oldest if name not in package_index: raise ValueError(f"Package {name!r} not found in package index") sorted_versions = get_sorted_versions(name, package_index) # Filter versions that satisfy the current constraint candidate_versions = [ v for v in sorted_versions if version_satisfies(v, op, ver) ] if not candidate_versions: raise ValueError( f"No version of {name} satisfies constraint {name}{op or ''}{ver or ''}" ) # Also filter against all other pending requirements for the same package # Collect all constraints for this package from pending_requirements same_pkg_constraints = [(n2, o2, v2) for n2, o2, v2 in rest if n2 == name] # Filter candidates against all known constraints for this package filtered_candidates = [] for v in candidate_versions: if all(version_satisfies(v, o2, v2) for n2, o2, v2 in same_pkg_constraints): filtered_candidates.append(v) if not filtered_candidates: raise ValueError( f"No version of {name} satisfies all constraints: " f"{[(name + (o2 or '') + (v2 or '')) for _, o2, v2 in same_pkg_constraints + [(name, op, ver)]]}" ) # Remove same-package constraints from rest since we'll handle them now rest_without_same = [(n2, o2, v2) for n2, o2, v2 in rest if n2 != name] last_error = None for candidate in filtered_candidates: # Try this candidate version new_resolved = dict(resolved) new_resolved[name] = candidate # Get dependencies of this version deps = package_index[name][candidate] new_pending = [] for dep_str in deps: dep_name, dep_op, dep_ver = parse_requirement(dep_str) new_pending.append((dep_name, dep_op, dep_ver)) # Add remaining requirements new_pending.extend(rest_without_same) try: result = backtrack(new_resolved, new_pending, visited_states) return result except ValueError as e: last_error = e continue if last_error: raise ValueError( f"Could not resolve {name}: tried all candidates. Last error: {last_error}" ) raise ValueError(f"Could not resolve {name}: no candidates available") # Parse initial requirements parsed_reqs = [] for req_str in requirements: name, op, ver = parse_requirement(req_str) parsed_reqs.append((name, op, ver)) return backtrack({}, parsed_reqs) # Example usage and testing if __name__ == '__main__': package_index = { "A": { "1.0.0": ["B<2.0.0"], "1.1.0": ["B<3.0.0"], "1.2.0": ["C==1.0.0"] }, "B": { "1.5.0": [], "2.5.0": [] }, "C": { "1.0.0": ["B>=2.0.0"], "1.1.0": [] } } # Test 1: A>=1.1.0 should resolve to A=1.2.0, C=1.0.0, B=2.5.0 result = resolve(["A>=1.1.0"], package_index) print("Test 1:", result) assert result == {'A': '1.2.0', 'C': '1.0.0', 'B': '2.5.0'}, f"Expected A=1.2.0, C=1.0.0, B=2.5.0, got {result}" # Test 2: A==1.1.0 should resolve to A=1.1.0, B=2.5.0 (latest B < 3.0.0) result2 = resolve(["A==1.1.0"], package_index) print("Test 2:", result2) assert result2 == {'A': '1.1.0', 'B': '2.5.0'}, f"Expected A=1.1.0, B=2.5.0, got {result2}" # Test 3: Conflict - A==1.2.0 needs C==1.0.0 which needs B>=2.0.0, but also B<2.0.0 try: result3 = resolve(["A==1.2.0", "B<2.0.0"], package_index) print("Test 3 (should have failed):", result3) except ValueError as e: print("Test 3 (expected conflict):", e) print("All tests passed!")

Result

#2

Winning Votes

1 / 3

Average Score

72
Judge Models OpenAI GPT-5.2

Total Score

60

Overall Comments

Provides a backtracking resolver with decent parsing via regex and includes example tests. However, it does not properly accumulate and enforce constraints discovered after a package is already resolved: if a later dependency introduces a tighter constraint on an already-resolved package, the resolver can wrongly fail instead of backtracking to try a different version of the depending package. It also includes an unused visited_states and lacks a robust strategy to select versions when constraints evolve, making it incorrect on common scenarios.

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Correctness

Weight 35%
48

Can resolve simple cases, but fails to properly backtrack when constraints for an already-resolved package are introduced later (it errors instead of revisiting earlier choices). This leads to incorrect failure on solvable graphs where later constraints force choosing a different version of a previously chosen package.

Completeness

Weight 20%
64

Implements parsing, backtracking, and some multi-constraint filtering for the same package within pending requirements. Includes sample tests. But lacks a complete constraint-propagation approach needed for realistic resolution, and visited_states is unused.

Code Quality

Weight 20%
69

Readable, modular helpers and clear test scaffolding. Minor issues: unused imports (cmp_to_key), unused visited_states, and an error message that embeds a Python list representation for constraints.

Practical Value

Weight 15%
58

Useful as a teaching example and passes included tests, but the constraint-handling flaw makes it unreliable in practice for many real dependency graphs where constraints accumulate over time.

Instruction Following

Weight 10%
78

Follows the signature and raises ValueError with messages; aims for latest versions. Some conflict messages are less clear and it includes a __main__ test block (not harmful) but core algorithm behavior diverges from the requirement to find a valid set when one exists.

Total Score

87

Overall Comments

Answer B delivers a robust and efficient dependency resolver. Its parsing functions (`parse_requirement`, `parse_version`) are more robust, especially with the use of regex. The backtracking algorithm is well-implemented, and its state management (passing `pending_requirements` as a list) is more efficient than deep-copying large dictionaries. It also includes a good optimization by filtering candidates against all known constraints for a package before recursing. While there are minor unused imports (`cmp_to_key`) and an unused `visited_states` parameter, these do not detract significantly from the overall quality and correctness.

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Correctness

Weight 35%
90

The solution correctly implements the dependency resolution algorithm, including transitive dependencies and version conflicts. Version parsing and requirement parsing are robust, and the logic for selecting the latest compatible versions is sound.

Completeness

Weight 20%
90

The solution fully addresses all aspects of the prompt, including transitive dependencies, version conflicts, and prioritizing the latest versions. It handles edge cases like missing packages and empty inputs gracefully. The algorithm is complete in its exploration of the dependency tree.

Code Quality

Weight 20%
80

The code is well-organized, readable, and uses appropriate data structures. The use of regex for parsing requirements is a good practice. Minor issues include an unused `visited_states` parameter and an unused `cmp_to_key` import, but these are small flaws in an otherwise high-quality implementation.

Practical Value

Weight 15%
85

This solution offers high practical value due to its robust parsing, efficient state management for backtracking (avoiding costly deep copies), and an optimization to consolidate constraints for a package. These aspects make it more suitable for real-world dependency resolution tasks.

Instruction Following

Weight 10%
90

The solution meticulously follows all instructions, from the function signature and return type to the specific handling of version specifiers and error conditions. It successfully implements a sound and efficient algorithm, prioritizing the latest versions as requested.

Total Score

70

Overall Comments

Answer B provides a working backtracking resolver with clear structure and good documentation. It uses regex for parsing requirements which is more robust. It includes inline test cases which demonstrate correctness. However, it processes requirements one at a time from a queue rather than using constraint propagation, which is less efficient. It has a visited_states parameter that is never actually used. The approach of filtering candidates against same-package constraints from the pending list is a reasonable optimization. However, it lacks the MRV heuristic that Answer A has, making it potentially less efficient on complex dependency graphs. The circular dependency handling is also implicit. One concern is that it could potentially miss constraints that arrive later in the pending queue for the same package if they come from different dependency chains.

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Correctness

Weight 35%
70

Answer B correctly resolves the given example and includes test assertions that verify correctness. However, the sequential processing of requirements could potentially miss constraint interactions in more complex scenarios where constraints for the same package come from different branches of the dependency tree.

Completeness

Weight 20%
70

Answer B handles missing packages, version conflicts, transitive dependencies, and all operators. It includes a visited_states parameter suggesting awareness of circular dependencies but never actually uses it. It includes comprehensive test cases demonstrating various scenarios.

Code Quality

Weight 20%
70

Answer B uses regex for parsing which is more robust. The code is well-documented with docstrings and type descriptions. However, the unused visited_states parameter is a code smell. The inline test cases are a nice touch for demonstrating usage. The overall structure is clear but slightly less elegant than A's constraint-based approach.

Practical Value

Weight 15%
65

Answer B's sequential approach is simpler to understand but less efficient for complex dependency graphs. The included test cases add practical value for verification. However, the lack of the MRV heuristic means it could be significantly slower on moderately complex real-world scenarios.

Instruction Following

Weight 10%
75

Answer B follows all instructions: returns the correct dict format, raises ValueError with messages, handles all operators, selects latest versions first, and handles transitive dependencies. The included test cases go beyond requirements but demonstrate compliance.

Comparison Summary

Final rank order is determined by judge-wise rank aggregation (average rank + Borda tie-break). Average score is shown for reference.

Judges: 3

Winning Votes

2 / 3

Average Score

76
View this answer

Winning Votes

1 / 3

Average Score

72
View this answer

Judging Results

Why This Side Won

Answer A wins primarily due to its more sophisticated algorithm design. It uses a constraint map that accumulates all constraints for each package, combined with the MRV (minimum remaining values) heuristic that selects the package with fewest candidates first. This is a well-known optimization for constraint satisfaction problems that significantly improves efficiency. Answer B processes requirements sequentially from a queue, which can miss constraint interactions and is generally less efficient. Both answers are correct for the given example and handle basic edge cases, but Answer A's approach is more robust for complex dependency graphs. Answer A also has slightly better error messages that list all conflicting constraints for a package.

Why This Side Won

Answer B is superior due to its more robust parsing logic using regular expressions and a more efficient state management strategy for the backtracking algorithm. The `deepcopy` operation in Answer A, performed at every recursive step, can introduce significant performance bottlenecks for larger dependency graphs, making Answer B's approach more practical and scalable. Answer B also includes a valuable optimization by consolidating all constraints for a package before attempting to resolve it.

Judge Models OpenAI GPT-5.2

Why This Side Won

Answer A correctly maintains a global constraint set per package and backtracks when new dependency constraints make a choice invalid, enabling it to adjust earlier decisions. Answer B can get stuck with an early high-version choice and then raise a conflict when later constraints appear, without reconsidering the earlier choice; this is a correctness-critical flaw for dependency resolution.

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