1696b92bb8
Sprints 46-58 implement the cross-language porting foundation: language-to-IR adapters, equivalence checking, gate validation, legacy ingestion, managed/dynamic families, low-level/logic-actor semantics, debug workflow tooling, AST-native family tools, Rust/CPP raising tools, system-level orchestration, query family, and porting gates. Sprint 59 adds the governance layer (policy packs, review boards, waiver packets, ambiguity triage, decision ledger) with the whetstone_review_porting_decision MCP tool. Also includes: sprint plans 46-130, MCP taskitem pipeline scripts, CLAUDE.md, docs, and full test matrix (steps 689-828). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
20 lines
2.0 KiB
C++
20 lines
2.0 KiB
C++
// Step 725: Behavioral divergence packet + minimization (8 tests)
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#include "equiv/BehavioralDivergencePacket.h"
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#include <iostream>
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static int p=0,f=0;
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#define T(n) { std::cout<<" "<<#n<<"... "; }
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#define P() { std::cout<<"PASS\n"; ++p; }
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#define F(m) { std::cout<<"FAIL: "<<m<<"\n"; ++f; }
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#define C(c,m) if(!(c)){F(m);return;}
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void t1(){T(equivalent_classification);auto packet=BehavioralDivergencePacketModel::classify("c","x","x","input");C(packet.classification=="equivalent","class");P();}
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void t2(){T(divergence_classification);auto packet=BehavioralDivergencePacketModel::classify("c","x","y","input");C(packet.classification=="unacceptable_divergence","class");P();}
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void t3(){T(minimize_short_input_kept);auto packet=BehavioralDivergencePacketModel::classify("c","x","y","short");C(packet.minimizedInput=="short","min");P();}
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void t4(){T(minimize_long_input_cut);auto packet=BehavioralDivergencePacketModel::classify("c","x","y","0123456789abcdefXYZ");C(packet.minimizedInput.size()==16,"min");P();}
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void t5(){T(case_id_preserved);auto packet=BehavioralDivergencePacketModel::classify("cid","x","y","in");C(packet.caseId=="cid","id");P();}
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void t6(){T(json_shape);auto j=BehavioralDivergencePacketModel::toJson(BehavioralDivergencePacketModel::classify("c","x","y","in"));C(j.contains("case_id")&&j.contains("classification")&&j.contains("minimized_input"),"shape");P();}
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void t7(){T(machine_readable);auto j=BehavioralDivergencePacketModel::toJson(BehavioralDivergencePacketModel::classify("c","x","y","in"));C(j.is_object(),"obj");P();}
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void t8(){T(deterministic);auto a=BehavioralDivergencePacketModel::toJson(BehavioralDivergencePacketModel::classify("c","x","y","input_value")).dump();auto b=BehavioralDivergencePacketModel::toJson(BehavioralDivergencePacketModel::classify("c","x","y","input_value")).dump();C(a==b,"det");P();}
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int main(){std::cout<<"Step 725: BehavioralDivergencePacket\n";t1();t2();t3();t4();t5();t6();t7();t8();std::cout<<"\nResults: "<<p<<"/"<<(p+f)<<" passed\n";return f?1:0;}
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