Automated bisecting tool for regression detection

A computer-implemented method comprising:

• receiving initial parameters including a first commit and a second commit, wherein the first commit and the second commit comprise endpoints of an interval;
• identifying a midpoint between the first commit and the second commit, wherein the midpoint represents a commit to be tested;
• executing a test of the commit at the midpoint;
• determining, using a regression detection algorithm, whether a regression is present at the midpoint, wherein the regression detection algorithm automatically identifies patterns from test results indicative of regressions and detects regressions based on the identified patterns, the identified patterns being associated with a specified symptom occurring at the midpoint when the regression is a functional regression and associated with benchmark samples collected at the midpoint when the regression is a performance regression;
• setting the midpoint as the first commit or the second commit based on whether the regression is present at the midpoint, wherein the setting updates the initial parameters;
• identifying a new midpoint between the updated initial parameters, wherein the new midpoint represents a new commit to be tested;
• and
• automatically iteratively refining a search space of commit points using the new midpoint at each iteration until a single commit is identified, wherein the single commit corresponds to a commit causing the regression.

A non-transitory computer readable medium storing instructions, which when executed by at least one processor, result in operations comprising:

• receiving initial parameters including a first commit and a second commit, wherein the first commit and the second commit comprise endpoints of an interval;
• identifying a midpoint between the first commit and the second commit, wherein the midpoint represents a commit to be tested;
• executing a test of the commit at the midpoint;
• determining, using a regression detection algorithm, whether a regression is present at the midpoint, wherein the regression detection algorithm automatically identifies patterns from test results indicative of regressions and detects regressions based on the identified patterns, the identified patterns being associated with a specified symptom occurring at the midpoint when the regression is a functional regression and associated with benchmark samples collected at the midpoint when the regression is a performance regression;
• setting the midpoint as the first commit or the second commit based on whether the regression is present at the midpoint, wherein the setting updates the initial parameters;
• identifying a new midpoint between the updated initial parameters, wherein the new midpoint represents a new commit to be tested;
• and
• automatically iteratively refining a search space of commit points using the new midpoint at each iteration until a single commit is identified, wherein the single commit corresponds to a commit causing the regression.