Multi-Version Behavior Comparator

Differential testing, generalized: run N versions on the same inputs, and any disagreement is a bug somewhere. You don't need an oracle — the versions are each other's oracle.

Compared to → behavior-preservation-checker (yes/no answer, two versions): this handles N versions, and focuses on characterizing divergence, not just detecting it.

What counts as "behavior"

Pick which observables matter before running. Diffing everything produces noise.

Input generation — where the coverage comes from

Differential testing is only as good as the inputs. Sources, in order of effort:

1. Existing test inputs. If there's a test suite, extract the inputs. They're already interesting.
2. Boundary sweep. Empty, single element, max size, zero, negative, None/null, unicode, very long strings. The usual suspects.
3. Random/property-based. Hypothesis, QuickCheck. Great at finding the 17-element list where version A and B disagree.
4. Coverage-guided. Use coverage of version A (or B) to guide input generation toward unexercised branches. AFL-style but for diffing.
5. Mined from production. Logs, traces, recorded requests. Real-world distribution.

Normalization — avoiding false positives

Two outputs can be "the same" without being byte-identical:

Worked example — 3-way comparison

Setup: Three implementations of urlparse — stdlib, a hand-rolled one from legacy code, and a port to Go (via subprocess).

Harness:

import subprocess, json, urllib.parse
from hypothesis import given, strategies as st
from legacy import parse_url as legacy_parse

def normalize(result: dict) -> dict:
    # Port returns "" for missing, stdlib returns None. Unify.
    return {k: (v if v else None) for k, v in result.items()}

def run_go(url: str) -> dict:
    out = subprocess.run(["./go_urlparse"], input=url, capture_output=True, text=True)
    return json.loads(out.stdout)

@given(st.text(alphabet=st.characters(blacklist_categories=["Cs"]), max_size=200))
def test_three_way(url):
    try:
        std = urllib.parse.urlparse(url)._asdict()
    except Exception as e:
        std = {"__error__": type(e).__name__}
    try:
        leg = legacy_parse(url)
    except Exception as e:
        leg = {"__error__": type(e).__name__}
    go = run_go(url)

    std_n, leg_n, go_n = normalize(std), normalize(leg), normalize(go)

    if not (std_n == leg_n == go_n):
        # Majority vote: 2-of-3 agree → the odd one out is probably wrong
        if std_n == leg_n: suspect = "go"
        elif std_n == go_n: suspect = "legacy"
        elif leg_n == go_n: suspect = "stdlib"  # unlikely but possible
        else: suspect = "all disagree"
        print(f"DIVERGENCE on {url!r}: suspect={suspect}")
        print(f"  stdlib: {std_n}\n  legacy: {leg_n}\n  go:     {go_n}")

Findings after 10k inputs:

Legacy has IPv6 bracket handling wrong. Go is too strict on percent-decoding. Stdlib agrees with 2-of-3 every time — it's the reference.

Majority voting

With 3+ versions, disagreement is localizable: if 2 agree and 1 differs, the 1 is probably wrong. Not always — maybe the 2 share a bug. But it's a strong prior. Rank bugs by (minority size, input simplicity).

Do not

• Do not diff string representations of structured data. {"a":1,"b":2} ≠ {"b":2,"a":1} as strings, = as dicts. Parse then compare.
• Do not treat every timing difference as a regression. 2× slower on one input is noise. 100× slower on all inputs is a regression.
• Do not stop at the first divergence. Collect all divergences, then cluster by root cause. Ten failing inputs might be one bug.
• Do not forget to test the error paths. Both versions handle valid inputs fine and diverge on None, empty, or malformed input — that's where bugs live.

Output format

## Versions compared
A: <description/path/commit>
B: ...
C: ...

## Observables
<what was diffed — return value, exceptions, side effects>

## Normalization
<what was canonicalized before comparison>

## Input space
<how inputs were generated — boundary, random, mined>
<N inputs tested>

## Divergences
| Input | A | B | C | Suspect | Cluster |
| ----- | - | - | - | ------- | ------- |

## Clustered root causes
1. <cause>: <N> divergences, suspect=<version>
   Example input: <minimal reproducer>