gotools/internal/diffp/diff.go

type pair …

// Diff returns an anchored diff of the two texts old and new
// in the “unified diff” format. If old and new are identical,
// Diff returns a nil slice (no output).
//
// Unix diff implementations typically look for a diff with
// the smallest number of lines inserted and removed,
// which can in the worst case take time quadratic in the
// number of lines in the texts. As a result, many implementations
// either can be made to run for a long time or cut off the search
// after a predetermined amount of work.
//
// In contrast, this implementation looks for a diff with the
// smallest number of “unique” lines inserted and removed,
// where unique means a line that appears just once in both old and new.
// We call this an “anchored diff” because the unique lines anchor
// the chosen matching regions. An anchored diff is usually clearer
// than a standard diff, because the algorithm does not try to
// reuse unrelated blank lines or closing braces.
// The algorithm also guarantees to run in O(n log n) time
// instead of the standard O(n²) time.
//
// Some systems call this approach a “patience diff,” named for
// the “patience sorting” algorithm, itself named for a solitaire card game.
// We avoid that name for two reasons. First, the name has been used
// for a few different variants of the algorithm, so it is imprecise.
// Second, the name is frequently interpreted as meaning that you have
// to wait longer (to be patient) for the diff, meaning that it is a slower algorithm,
// when in fact the algorithm is faster than the standard one.
func Diff(oldName string, old []byte, newName string, new []byte) []byte { … }

// lines returns the lines in the file x, including newlines.
// If the file does not end in a newline, one is supplied
// along with a warning about the missing newline.
func lines(x []byte) []string { … }

// tgs returns the pairs of indexes of the longest common subsequence
// of unique lines in x and y, where a unique line is one that appears
// once in x and once in y.
//
// The longest common subsequence algorithm is as described in
// Thomas G. Szymanski, “A Special Case of the Maximal Common
// Subsequence Problem,” Princeton TR #170 (January 1975),
// available at https://research.swtch.com/tgs170.pdf.
func tgs(x, y []string) []pair { … }