tester push_swap

A focused tool for developers and students to test push_swap sorting sequences and verify operation counts and correctness.

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About This Tool

tester push_swap is a focused analysis tool that simulates the push_swap problem to validate sorting sequences and measure efficiency. It accepts explicit A and B stacks or generated test cases and returns detailed results suitable for debugging and learning.

Conceptually it models two stacks A and B. The simulator applies the standard operations (sa, sb, ss, pa, pb, ra, rb, rr, rra, rrb, rrr) to move elements between stacks until A is sorted or a limit is reached. It can validate a user-provided sequence or generate test sequences to explore corner cases.

Who benefits: students building intuition about stack-based sorting, educators validating assignments, and developers benchmarking push_swap solvers. The tool's traceable operation sequence and final state enable reproducible testing across environments.

Unique value: strict correctness checks, configurable input ranges, and explicit counts of operations vs. optimal expectations. Core features include input normalization, end-state verification, and a structured, exportable result set including final stacks, operation_count, and stepwise state transitions. Typical use cases include unit testing of sort sequences, regression testing when implementing new ops, and learning for algorithm analysis. It is intended for researchers and practitioners seeking deterministic evaluation of push_swap strategies.

How to Use

  1. Provide inputs: enter A and B as integer sequences.
  2. Configure mode and limits: set mode ('provided'|'generated'), max_operations, seed.
  3. Run analysis: start the simulation.
  4. Review outputs: examine final_state, operation_count, and operation_sequence.
  5. Export results: download or copy the trace for testing.
How to use tester push_swap

Frequently Asked Questions

Find Quick Answers

What inputs are required?
Inputs include A and B stacks as sequences of integers, a mode selector (provided or generated), an optional max_operations to cap computation, and an optional seed for reproducible random test generation.
What outputs does it return?
Outputs include the final_state with A and B, a boolean sorted_ok, the operation_count, the full operation_sequence, and an optional per_step_states log for detailed debugging.
Can it generate random tests?
Yes. In generated mode, the tool can create varied test cases using a seed; you control range, size, and distribution to explore edge cases, compare solver strategies, and validate robustness across environments.
How is correctness defined?
Correctness is defined as A being sorted in ascending order with B empty after applying a valid operation sequence; any deviation or illegal operation yields a failure indicator and a trace of the first offending step.

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