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Pixeebot – a GitHub App that fixes your Sonar findings (Java/Python)

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Pixeebot – a GitHub App that fixes your Sonar findings (Java/Python)

We made a bot, built on an [open source framework]( https://codemodder.io ), that fixes the problems Sonar (and others!) find in your code. Here are a handful of examples of pixeebot fixing Sonar findings on some OSS code: https://github.com/nahsra/cql-evaluator-fork/pull/1/files https://github.com/nahsra/bsights-engine-spark-fork/pull/1/f... https://github.com/pixee/pygoat/pull/2/files The changes aren't all super fancy, but we're orienting towards solving real problems and remediating issues -- grunt work you don't want to have to do, but compliance says you should (and you probably should)! Right now, we fix around 25 of the things that Sonar commonly finds (and a lot more that it doesn't find!). You can see the complete list of things we fix here: https://docs.pixee.ai/codemods/overview/ I'll tell you, it's so much nicer to receive PRs than tool warnings. To try it out: 1. Install the Pixeebot GitHub App on a Sonar-monitored GitHub repository - https://github.com/apps/pixeebot 2. Create a Sonar token that Pixeebot can use to read your findings - For SonarCloud users: go here https://sonarcloud.io/account/security - For other Sonar users, go to User > My Account > Security - Put that token in a SONAR_TOKEN repository secret 3. Add the GitHub Action that will connect Pixeebot to Sonar - Copy this sample action into your repo: https://github.com/pixee/upload-tool-results-action/blob/mai... - Set the Sonar URL if you're using a locally hosted SonarQube Boom! Now, pixeebot will monitor your repository and thoughtfully send you PRs at the right moments. Like, when Sonar finds some issues in your new PR, or when we think there's a good opportunity to address something in your backlog. We've got so much more in the pipeline I'd love to tell you about, but we're eager to get feedback on what we've got so far for Sonar users. P.S. By the way, we're a small team but we offer all the right security and compliance controls a company might want: https://trust.pixee.ai/ .

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, new · Missing: mac, agents, macos
87%87% 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 · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, pipe · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, users, way · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
30%30% 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
15%15% 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.

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