Answer A: OpenAI GPT-5 mini
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
Winning Votes
2 / 3
Average Score
Total Score
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%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%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%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%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%Matches the requested function signature, returns a lockfile dict, prefers latest versions, and raises ValueError on conflicts with reasonably clear messages.
Total Score
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%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%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%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%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%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
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%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%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%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%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%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.