Software Alternatives, Accelerators & Startups
Register | Login

Cucumber VS SciPy

Compare Cucumber VS SciPy and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Cucumber logo Cucumber

Cucumber is a BDD tool for specification of application features and user scenarios in plain text.

SciPy logo SciPy

SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. 
  • Cucumber Landing page
    Landing page //
    2022-01-19
  • SciPy Landing page
    Landing page //
    2023-07-26

Cucumber features and specs

  • Behavior-Driven Development (BDD) Framework
    Cucumber supports BDD, allowing collaboration between developers, testers, and non-technical stakeholders to improve the quality of development through clear specifications.
  • Gherkin Syntax
    Utilizes the Gherkin language to write test cases in plain English, making them more readable and understandable for non-technical team members.
  • Integrates with Other Tools
    Easily integrates with other testing and development frameworks like JUnit, TestNG, and Selenium, enhancing its flexibility and utility.
  • Open Source
    As an open-source tool, Cucumber allows for extensive customization and community support, reducing the cost of setting up a testing framework.
  • Supports Multiple Languages
    Offers support for various programming languages including Java, Ruby, and JavaScript, making it versatile for different project needs.

Possible disadvantages of Cucumber

  • Steep Learning Curve
    Requires a good understanding of both BDD practices and Cucumber’s structure, which might be challenging for beginners.
  • Performance Overheads
    Execution of Cucumber tests can be slower compared to other testing frameworks, making it less ideal for very large projects requiring fast feedback loops.
  • Verbose Code
    Writing tests in Gherkin can lead to more verbose code, which might require additional maintenance and can become cumbersome over time.
  • Dependency Management
    Managing dependencies for integrating Cucumber with other testing frameworks can be complex, requiring careful coordination.
  • Not Ideal for Unit Testing
    Cucumber is more suited for acceptance and integration testing rather than unit testing, potentially necessitating additional tools for a comprehensive testing strategy.

SciPy features and specs

  • Comprehensive Library
    SciPy provides a wide range of scientific and technical computing tools, including modules for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and more.
  • Interoperability
    SciPy is built on top of NumPy, which means it naturally dovetails with other scientific computing libraries in the Python ecosystem, facilitating ease of integration and use in conjunction with libraries like Matplotlib and Pandas.
  • Active Community
    SciPy boasts a large, active community of developers and users, which provides extensive documentation, forums, and regular updates and improvements to the library.
  • Open-source
    Being an open-source library, SciPy promotes collaboration and adaptation, allowing users to contribute to its development and modify its tools to suit specific needs.

Possible disadvantages of SciPy

  • Complexity
    For beginners in scientific computing or programming, the comprehensive nature of SciPy can be overwhelming due to its broad range of functionalities and somewhat steep learning curve.
  • Performance Limitations
    Being a high-level library, SciPy may not be as performant as low-level implementations or specialized tools for very demanding computational tasks or large-scale data processing.
  • Dependency on NumPy
    While SciPy's reliance on NumPy ensures compatibility and ease of use within the Python ecosystem, it also means that its performance and limits are tied to those of NumPy.
  • Windows Limitations
    Some functions and modules of SciPy may not work as efficiently or might encounter compatibility issues when run on Windows operating systems compared to Unix-based systems.

Analysis of Cucumber

Overall verdict

  • Yes, Cucumber (cukes.info) is generally considered a good tool for behavior-driven development (BDD).

Why this product is good

  • Cucumber is highly regarded because it allows teams to write tests in plain language that can be understood by all stakeholders, regardless of technical expertise. This enhances communication and collaboration between developers, testers, and business professionals. Furthermore, it supports various programming languages and integrates well with other tools, making it versatile and adaptable to different engineering environments.

Recommended for

  • Teams practicing behavior-driven development (BDD)
  • Projects that require clear communication between non-technical and technical team members
  • Development environments where automated testing is an integral part of the process
  • Organizations aiming to improve collaboration and understanding across departments

Cucumber videos

+ Add

Madam Kilay Skin Magical Review / Orange cucumber review

More videos:

  • Review - Puff Bar - Cucumber Review (Best Disposable Vape Brand)
  • Review - THE CUCUMBER CHALLENGE! (1 MILLION SUBSCRIBER SPECIAL)
  • Tutorial - Cucumber automation suit

SciPy videos

+ Add

Numerical Computing With NumPy Tutorial | SciPy 2020 | Eric Olsen

More videos:

  • Tutorial - Land on Vector Spaces: Practical Linear Algebra with Python | SciPy 2019 Tutorial | L Barba, T Wang

Category Popularity

0-100% (relative to Cucumber and SciPy)

Cucumber

SciPy

Automated Testing
100 100%
Automated Testing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Browser Testing
100 100%
Browser Testing
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Cucumber and SciPy. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Cucumber and SciPy

Cucumber Reviews

Top Selenium Alternatives
Cucumber itself is not a test automation tool but a framework that supports BDD. It is often used in conjunction with Selenium to provide a layer where test scenarios are written in a way that is understandable by all team members. Unlike Selenium, which focuses on automating browser actions, Cucumber focuses on defining behavior and can be used to drive Selenium tests.
Source: bugbug.io
5 Selenium Alternatives to Fill in Your Top Testing Gaps
Business testers are likely to prefer to use Cucumber over Selenium since script Cucumber lets you write test scenarios using a plain-English scripting language called Gherkin. Using Gherkin instead of code makes test script creation a much simpler process, since anyone can read, write, and understand the scripts regardless of testing experience.
Source: www.perfecto.io
Top 20 Best Automation Testing Tools in 2018 (Comprehensive List)
Cucumber is an open-source tool that is designed over the concept of BDD (Behavior-driven development). It is used to perform the automated acceptance testing by running the examples that best describe the behavior of the application. It gets you a single up-to-date living document that is having both specification and test documentation.
Source: www.softwaretestinghelp.com

SciPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
SciPy is primarily used for mathematical and scientific computations, but sometimes it can also be used for basic image manipulation and processing tasks using the submodule scipy.ndimage.At the end of the day, images are just multidimensional arrays, SciPy provides a set of functions that are used to operate n-dimensional Numpy operations. SciPy provides some basic image...
Source: www.analyticsvidhya.com

Social recommendations and mentions

Based on our record, SciPy seems to be a lot more popular than Cucumber. While we know about 17 links to SciPy, we've tracked only 1 mention of Cucumber. 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.

Cucumber mentions (1)

SciPy mentions (17)

  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Video Generation with Python
    Python has become a popular programming language for different applications, including data science, artificial intelligence, and web development. But, did you know creating and rendering fully customized videos with Python is also possible? At Stack Builders, we have successfully used Python libraries such as MoviePy, SciPy, and ImageMagick to generate videos with animations, text, and images. In this article, we... - Source: dev.to / over 1 year ago
  • Beginning Python: Project Management With PDM
    A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / over 1 year ago
  • Understanding Cosine Similarity in Python with Scikit-Learn
    SciPy: a library used for scientific and technical computing. It has a function that can calculate the cosine distance, which equals 1 minus the cosine similarity. - Source: dev.to / about 2 years ago
  • PSA: You don't need fancy stuff to do good work.
    Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: about 2 years ago
View more

What are some alternatives?

When comparing Cucumber and SciPy, you can also consider the following products

Selenium - Selenium automates browsers. That's it! What you do with that power is entirely up to you. Primarily, it is for automating web applications for testing purposes, but is certainly not limited to just that.

NumPy - NumPy is the fundamental package for scientific computing with Python

Robot framework - Robot Framework is a generic test automation framework for acceptance testing and acceptance...

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

JUnit - JUnit is a simple framework to write repeatable tests.

Matplotlib - matplotlib is a python 2D plotting library which produces publication quality figures in a variety...

Loading...