Flay analyzes code for structural similarities. Differences in literal values, variable, class, method names, whitespace, programming style, braces vs do/end, etc are all ignored. Making this totally rad. == Features/Problems: * Reports differences at any level of code. * Adds a score multiplier to identical nodes. * Differences in literal values, variable, class, and method names are ignored. * Differences in whitespace, programming style, braces vs do/end, etc are ignored. * Works across files. * Add the flay-persistent plugin to work across large/many projects. * Run --diff to see an N-way diff of the code. * Provides conservative (default) and --liberal pruning options. * Provides --fuzzy duplication detection. * Language independent: Plugin system allows other languages to be flayed. * Ships with .rb and .erb. * javascript and others will be available separately. * Includes FlayTask for Rakefiles. * Uses path_expander, so you can use: * dir_arg -- expand a directory automatically * @file_of_args -- persist arguments in a file * -path_to_subtract -- ignore intersecting subsets of files/directories * Skips files matched via patterns in .flayignore (subset format of .gitignore). * Totally rad.

Flog reports the most tortured code in an easy to read pain report. The higher the score, the more pain the code is in.

Analyze code for potentially uncalled / dead methods, now with auto-removal. == Features/Problems: * Static analysis of code. Can be easily hooked up to a CI. * As with all static analysis tools of dynamic languages, can't be 100%. * Whitelisting known good methods by name or regexp. * Use --rails for Rails-specific domain knowledge. * Use debride_rm to brazenly remove all unused methods. BE CAREFUL. * Use `debride_rails_whitelist` to generate an emperical whitelist from logs. * Uses path_expander, so you can use: * dir_arg -- expand a directory automatically * @file_of_args -- persist arguments in a file * -path_to_subtract -- ignore intersecting subsets of files/directories

Use the GoodData::Client class to integrate GoodData into your own application or use the CLI to work with GoodData directly from the command line.

Hunting down random test failures can be very very difficult, sometimes impossible, but minitest-bisect makes it easy. minitest-bisect helps you isolate and debug random test failures. If your tests only fail randomly, you can reproduce the error consistently by using `--seed <num>`, but what then? How do you figure out which combination of tests out of hundreds are responsible for the failure? You know which test is failing, but what others are causing it to fail or were helping it succeed in a different order? That's what minitest-bisect does best.