


@ 473,7 +473,7 @@ ZZ 




 









2) starting from the "good" ends of the graph, associate to each 




commit the number of ancestors it has plus one 




commit the number of ancestors it has plus one 









For example with the following graph where H is the "bad" commit and A 




and D are some parents of some "good" commits: 



@ 514,7 +514,7 @@ DE 




 









4) the best bisection point is the commit with the highest associated 




number 




number 









So in the above example the best bisection point is commit C. 








@ 580,8 +580,8 @@ good or a bad commit does not give more or less information). 









Let's also suppose that we have a cleaned up graph like one after step 




1) in the bisection algorithm above. This means that we can measure 




the information we get in terms of number of commit we can remove from 




the graph.. 




the information we get in terms of number of commit we can remove 




from the graph.. 









And let's take a commit X in the graph. 








@ 689,18 +689,18 @@ roughly the following steps: 




6) sort the commit by decreasing associated value 









7) if the first commit has not been skipped, we can return it and stop 




here 




here 









8) otherwise filter out all the skipped commits in the sorted list 









9) use a pseudo random number generator (PRNG) to generate a random 




number between 0 and 1 




number between 0 and 1 









10) multiply this random number with its square root to bias it toward 




0 




0 









11) multiply the result by the number of commits in the filtered list 




to get an index into this list 




to get an index into this list 









12) return the commit at the computed index 








