Optimize test suites using mutation testing to maximize mutation kill rate with minimal tests. Use when you need to reduce test suite size while maintaining quality, identify redundant tests, improve mutation coverage, or validate test effectiveness. The skill analyzes test coverage, execution intervals, and redundancy using mutation operators, selects or generates a minimal subset of tests that maximizes mutation kill rate, and outputs an optimized test suite with detailed reports showing killed and surviving mutants. Supports multiple programming languages and mutation testing frameworks.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill optimizes test suites using mutation testing, selecting a minimal subset of tests that maximizes mutation kill rate while reducing execution time and redundancy.
# Optimize test suite with mutation testing
python scripts/optimize.py /path/to/repo --tests tests/
# Use specific mutation operators
python scripts/optimize.py repo/ --tests tests/ --operators AOR,ROR,COR
# Generate optimization report
python scripts/optimize.py repo/ --tests tests/ --output report.json
Supports common mutation operators:
Generates detailed report with:
Example report:
{
"original_tests": 150,
"optimized_tests": 45,
"mutation_kill_rate": 92.5,
"killed_mutants": 185,
"surviving_mutants": 15,
"time_savings": "67%"
}
python scripts/optimize.py <repo_path> --tests <test_dir> [--operators <ops>] [--output <report.json>]
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Applies abstract interpretation using different abstract domains (intervals, octagons, polyhedra, sign, congruence) to statically analyze program variables and infer invariants, value ranges, and relationships. Use when analyzing program properties, inferring loop invariants, detecting potential errors, or understanding variable relationships through static analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.
日本語の概要は準備中です。原文の説明を表示しています。
Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without execution.
日本語の概要は準備中です。原文の説明を表示しています。
Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations. Highlights key control flow paths, variable relationships, loop invariants, function summaries, and potential runtime states using abstract domains (intervals, signs, nullness, etc.). Use when analyzing program behavior, understanding execution paths, computing loop invariants, tracking variable ranges, detecting potential runtime errors, or generating program summaries without concrete execution.
日本語の概要は準備中です。原文の説明を表示しています。
Create ACSL (ANSI/ISO C Specification Language) formal annotations for C/C++ programs. Use this skill when working with formal verification, adding function contracts (requires/ensures), loop invariants, assertions, memory safety annotations, or any ACSL specifications. Supports Frama-C verification and generates comprehensive formal specifications for C/C++ code.
日本語の概要は準備中です。原文の説明を表示しています。
CLI-based browser automation with persistent page state using ref-based element interaction. Use when users ask to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
日本語の概要は準備中です。原文の説明を表示しています。