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GitHub Issues vs froglogic Coco Code Coverage comparison

Qt Group and GitHub are both solutions in the Debugging category. Qt Group is ranked #6, while GitHub is ranked #7 with an average rating of 8.0. Qt Group holds a 8.6% mindshare in D, compared to GitHub’s 1.7% mindshare. Additionally, 100% of Qt Group users are willing to recommend the solution, compared to 100% of GitHub users who would recommend it.
froglogic Coco Code Coverage
Read 1 froglogic Coco Code Coverage review
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  • 151 Comparison Views
100% willing to recommend
GitHub Issues
Read 1 GitHub Issues review
  • 67 Views
  • 67 Comparison Views
100% willing to recommend

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

froglogic Coco Code Coverage
Ranking in Debugging
6th
Average Rating
7.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
GitHub Issues
Ranking in Debugging
7th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2025, in the Debugging category, the mindshare of froglogic Coco Code Coverage is 8.6%, up from 4.2% compared to the previous year. The mindshare of GitHub Issues is 1.7%, up from 0.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Debugging
 

Featured Reviews

AnirbanSarkar - PeerSpot reviewer
Integrates well, highly scalable, but more programming langue support needed
If someone is already using Squish as a regression tool, froglogic Coco Code Coverage can be a very handy add-on to that. If somebody is already having some mechanism to write their unit test cases, froglogic Coco Code Coverage can be a very good tool for giving the code coverage. The solution overall is still in development, they need to polish a few things moving forward. I rate froglogic Coco Code Coverage a seven out of ten.
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Devansh Singh - PeerSpot reviewer
Streamlining project management by facilitating issue tracking, collaboration, and transparency, allowing users to categorize, assign, and discuss tasks efficiently
In terms of scalability, we can include as many Issues as needed without encountering any apparent limits. Based on my observations from various open-source projects, even those with hundreds or thousands of Issues, there don't seem to be significant problems. There are around twenty to twenty-five members in the prototype.
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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"If somebody is using a kind of Squish as a regression testing tool, froglogic Coco Code Coverage can be used in parallel because these two particular products can communicate with one another."
"It enables mapping issues to specific team members, making it a valuable feature."
 

Cons

"froglogic Coco Code Coverage could improve by adding more computer programing language, it would be better. It only covers four languages."
"The challenge lies in the initial setup and the difficulty of adding new Issues."
 

Pricing and Cost Advice

Information not available
"It is an open source."
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Questions from the Community

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What do you like most about GitHub Issues?
It enables mapping issues to specific team members, making it a valuable feature.
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What is your experience regarding pricing and costs for GitHub Issues?
It is an open source. There are no extra charges associated with it.
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What needs improvement with GitHub Issues?
The challenge lies in the initial setup and the difficulty of adding new Issues.
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Comparisons

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GDB vs froglogic Coco Code Coverage
Compared 56% of the time
Polyspace Bug Finder vs froglogic Coco Code Coverage Logo
Polyspace Bug Finder vs froglogic Coco Code Coverage
Compared 44% of the time
More froglogic Coco Code Coverage Competitors
No data available
 

Overview

The Coco code coverage tool is a complete, cross-platform, cross-compiler tool chain allowing to analyze the test coverage of C, C++, C# and Tcl code.

Coco is used to

  • Find and highlight untested code sections
  • Find redundant tests (i.e., tests that merely duplicate others)
  • Find unreachable code
  • Compute the optimal order of test execution that will maximize the overall coverage
  • Compare the test coverage of two application versions or test executions.
Qt Group

Create issues, break them into tasks, track relationships, add custom fields, and have conversations. Visualize large projects as spreadsheets or boards, and automate everything with code.

GitHub
 

Sample Customers

Google, Nokia, Pfizer, Siemens, Synopsys, Airbus, Boeing, Mercedes Benz, Disney, Shell, Reuters, Vodafone, XILINX, GE, Ericsson
Information Not Available
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