To understand this properly, I suggest to look at distributions and how operations with distributions are defined in the first place.
A distribution is an object that acts on the space of infinitely differentiable and compactly supported functions in a linear and continuous way (check a textbook or Wikipedia for the precise definition). I.e. a distribution $T$ on a set $\Omega\subset\newcommand{\RR}{\mathbb{R}}\RR^n$assigns to any infinitely differentiable and compactly supported function $\phi$ defined on $\Omega$ a complex number $T(\phi)$. Then one notes that locally integrable function $f$ defined on $\Omega$ induced a distribution $T_f$ via the operation
$$T_f(\phi) = \int_\Omega f(x)\phi(x)dx.$$
Now one can try to define operations which one can do to a locally integrable function also for a distribution by analogy. Take, for example, translation (in the case $\Omega=\RR^n$): define the operation $t_y(f)$ defined by $t_y(f)(x) = f(x-y)$. Observe that
$$T_{t_y(f)}(\phi) = \int t_y(f)(x)\phi(x)dx = \int f(x-y)\phi(x)dx = \int f(x)\phi(x+y)dx = T_f(t_{-y}(\phi)).$$
I.e. "translating the function $f$ is the same as translation the test function $\phi$ in the opposite direction". Hence, one defines the translation of the distribution $T$ as
$$t_y(T)(\phi) := T(t_{-y}\phi).$$
(Work out, that the translation of the Dirac $\delta$ is what you think it should be.) Now you should be able to do the same thing with scaling $s_a(f)(x) = f(ax)$.