MLLib GenerateLinearInput has 'interesting' constant multiplication factor

Consider the generateLinearInput method from MLLib LinearDataGenerator:

Here is the signature of the method:

  def generateLinearInput(
      intercept: Double,
      weights: Array[Double],
      xMean: Array[Double],
      xVariance: Array[Double],
      nPoints: Int,
      seed: Int,
      eps: Double): Seq[LabeledPoint] = {

and here is the core logic for generating the raw data points:

val rnd = new Random(seed)
val x = Array.fill[Array[Double]](nPoints)(
  Array.fill[Double](weights.length)(rnd.nextDouble()))

x.foreach { v =>
  var i = 0
  val len = v.length
  while (i < len) {
    v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i)
    i += 1
  }

Notice in particular the 12.0 scaling factor on the variance. What is the purpose of that factor?

For completeness: here is the remainder of that method - in which the input linear function is applied to the x/domain values to generate the output y/range values:

val y = x.map { xi =>
  blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps * rnd.nextGaussian()
}
y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2)))