The Proposal

(Idea originally suggested by Mel Gorman)

There are customer use cases where sharing information via internet or uploading data somewhere is not acceptable for security reasons: this avoid the usage of some tool like the most famous Google Analytics, and prevent developers from understanding how the web application is used by the customers. I don't want to reinvent the wheel and re-implement a copy of Google Analytics, but getting inspired from it, the goal is to reuse information that we already have to extrapolate an analysis of the WebApp customer usage. I started this project with the aim of learning a programming language where I am not so comfortable yet (python). The purpose of this Hack Week project is to bring this basic tool at a minimal stable and usable state with the purpose of analyze the usage of a WebApp in scenarios where the WebApp is used in an internal network only (offline, disconnected from the internet).

Bisection is a well known method of localizing which commit caused a regression in a code repository. git-bisect is a particularly used tool for this problem in git repositories. However it is often the case that the failure is probabilistic in nature - either because we don't have a reliable reproducer of the failure and thus not reproducing a problem on a particular commit does not mean the problem is not still present there, or because of inherent variability of e.g. performance regressions. Bisection for such failures is problematic as it takes only one false result for the bisection to end up in an unrelated part of code history. So in these cases we usually have to heavily extend runtime of a reproducer or do multiple test runs or multiple bisection runs to minimize a chance of error. The aim of the project is to implement stochastic bisection for git. I.e., a method that will count with the fact that test results at each point of code history have some error rate and provide points in code history to test to find commit in code history that is with high probability introducing the regression in the smallest possible number of tests. Then we can use this method for bisection of performance problems in our performance testing grid Marvin.