Updated.

README.md

@@ -52,6 +52,24 @@ This command creates the file `bigO.pdf` that contains graphs like this:
 
 ### Technical Details
 
-`bigO`'s curve-fitting based approach is inspired by ["Measuring Empirical Computational Complexity"](https://theory.stanford.edu/~aiken/publications/papers/fse07.pdf) by Goldsmith et al., FSE 2007.
-
-To generate diversity even for fixed inputs, `bigO` uses a novel approach that randomly dilates both the execution time of called functions and memory allocations, in effect simulating the effect of different inputs.
+#### Curve-fitting
+
+`bigO`'s curve-fitting based approach is directly inspired by
+["Measuring Empirical Computational
+Complexity"](https://theory.stanford.edu/~aiken/publications/papers/fse07.pdf)
+by Goldsmith et al., FSE 2007, using log-log plots to fit a power-law distribution.
+
+Unlike that work, `bigO` uses the
+[AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion) to
+select the best model. `bigO` also measures space complexity by
+tracking memory allocations during function execution.
+
+#### Diversity via random time and space dilation
+
+While previous work depends on functions being executed over a wide
+range of inputs, `bigO` uses a novel approach that lets it generate
+diversity even for fixed inputs. This approach works by randomly
+dilating the execution time of functions called by the function being
+tested, as well as the size of memory allocations. This approach lets
+`bigO` collect more data points and better measure computational
+complexity.