Feature Flags Are Changing Observability As You Know It
There’s no incident response without visibility. You’ve probably implemented monitoring tools to report on your system, but how do you get from knowing what healthy performance looks like to quickly detecting and dealing with issues, all without false positives? Download this free white paper to learn more!
renderContent(deleteTreatment) {
const allowDelete = deleteTreatment.treatment === "on";
return (
<div className="todoListMain">
<div className="header">
<form onSubmit={this.addItem}>
<input
ref={(a) => (this._inputElement = a)}
placeholder="Enter Task"
></input>
<button type="submit">Add</button>
</form>
</div>
<TodoItems
entries={this.state.items}
allowDelete={allowDelete}
delete={this.deleteItem}
/>
<TodoItems
entries={this.state.items}
allowDelete={allowDelete}
delete={this.deleteItem}
/>
</div>
);
}Feature Flags: An Added Dimension
In modern software development, monitoring and observability must occur at the individual feature level. Why? Because when you track granular changes, you see the root cause behind each new effect — and that’s essential to stay on top of error detection and response.
Rollbacks rarely provide fast solutions
When rapid feature delivery meets a lack of visibility, this leads to more unchecked failures and, in turn, more emergency recovery measures that can bring further risk and inefficiency.
How to Compute the Confusion Matrix of Your Tests
You might have decided that 5% is too tight and changed the threshold for false positive rate when assuming the null hypothesis, a.k.a. the p-value, to 10%. Is there a simple way to explain the reason to test significance? How can you tell what is an appropriate threshold for your organization? With enough tests in your dataset, it’s time to draw a confusion matrix to decide what to do with that seemingly abstract number.
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