CodeClimate
Codacy
SonarQube
ESLint
CodeFactor.io
Coveralls
SensioLabs Insight
Source-Navigator NG
FuzzyWuzzy
Amazon Comprehend
Microsoft Bing Spell Check API
Google Cloud Natural Language API
spaCy
OpenNLP
Wordsmith
Microsoft Bing Autosuggest API
CodeClimate
FuzzyWuzzyProjects that require approximate string matching, such as natural language processing applications, data cleaning tasks, and developing user input systems where flexibility in matching is beneficial.
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Based on our record, CodeClimate should be more popular than FuzzyWuzzy. It has been mentiond 19 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Automated analysis tools: SonarQube, CodeClimate, and Codacy detect code-level debt automatically: cyclomatic complexity, code duplication, dependency staleness, and coverage gaps. These tools supplement but don't replace the architectural and business-logic debt that requires human judgment to identify and document. - Source: dev.to / 3 months ago
CodeClimate and Codacy can generate before/after metrics for code quality that make the starting and ending states concrete rather than subjective. - Source: dev.to / 3 months ago
CodeClimate quantifies maintainability so teams canโt hand-wave garbage away. - Source: dev.to / 11 months ago
Code Climate: Link - Automated code review and quality analysis for codebase health. - Source: dev.to / about 1 year ago
Use tools like SonarQube or CodeClimate to spot the high-risk 20%. Then fix one thing at a time not everything at once. This isnโt Dark Souls. - Source: dev.to / over 1 year ago
RapidFuzz ships several scorers โ see the rapidfuzz.fuzz docs for the full list. We use fuzz.WRatio (weighted ratio; same algorithm family as FuzzyWuzzyโs WRatio) because company names drift in different ways and no single metric covers all of them. - Source: dev.to / 3 months ago
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 3 years ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is ่ฒดๆนใใใใพใงใซๅพใฆใใๅใๅญๅใซ็บๆฎใใฆใใ ใใใญใ) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: almost 4 years ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: over 4 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 4 years ago
Codacy - Automatically reviews code style, security, duplication, complexity, and coverage on every change while tracking code quality throughout your sprints.
Amazon Comprehend - Discover insights and relationships in text
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
Microsoft Bing Spell Check API - Enhance your apps with the Bing Spell Check API from Microsoft Azure. The spell check API corrects spelling mistakes as users are typing.
ESLint - The fully pluggable JavaScript code quality tool
Google Cloud Natural Language API - Natural language API using Google machine learning