Bluecov – Code Coverage

Hacker News

Hi HN. Dissatisfied with the existing tools that didn't meet my use case, I've been working on creating a better code coverage app called Bluecov. Currently, it supports Java + JaCoCo coverage reports, which can be uploaded directly on the site. There is no upload tool at the moment, which means it won't integrate with any CI/CD tools, but that is a future goal. Other upcoming features include: * Graphs with coverage trends * A dashboard to provide a summary of all projects All logins are currently through GitHub, because I use the API to pull the source code. I do not store any source code nor save any GitHub API tokens so your code remains on GitHub's servers. I save only the information uploaded from JaCoCo and match that against the code in GitHub to display the code coverage results for each file. This is my first time deploying an app into the wild on my own, so I would appreciate any feedback and suggestions. I've also applied to YC S24 in hopes I can move to working on it full time.

Visit product View on Hacker NewsApr 25, 2024

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3points

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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