Copyright 2015 Alex Goussiatiner. All rights reserved. Use of this source code is governed by a MIT license that can be found in the LICENSE file. Godes is the general-purpose simulation library which includes the simulation engine and building blocks for modeling a wide variety of systems at varying levels of details. Godes Main Features: 1.Active Objects: All active objects in Godes shall implement the RunnerInterface 2.Random Generators: Godes contains set of built-in functions for generating random numbers for commonly used probability distributions. Each of the distrubutions in Godes has one or more parameter values associated with it:Uniform (Min, Max), Normal (Mean and Standard Deviation), Exponential (Lambda), Triangular(Min, Mode, Max) 3.Queues: Godes implements operations with FIFO and LIFO queues 4.BooleanControl : Godes uses BooleanControl variables as a locks for syncronizing execution of multiple runners 5.StatCollector: The object calculates and prints statistical parameters for set of samples collected during the simulation. See examples for usage.
Package hercules contains the functions which are needed to gather various statistics from a Git repository. The analysis is expressed in a form of the tree: there are nodes - "pipeline items" - which require some other nodes to be executed prior to selves and in turn provide the data for dependent nodes. There are several service items which do not produce any useful statistics but rather provide the requirements for other items. The top-level items include: - BurndownAnalysis - line burndown statistics for project, files and developers. - CouplesAnalysis - coupling statistics for files and developers. - ShotnessAnalysis - structural hotness and couples, by any Babelfish UAST XPath (functions by default). The typical API usage is to initialize the Pipeline class: Then add the required analysis: This call will add all the needed intermediate pipeline items. Then link and execute the analysis tree: Finally extract the result: The actual usage example is cmd/hercules/root.go - the command line tool's code. Hercules depends heavily on https://github.com/src-d/go-git and leverages the diff algorithm through https://github.com/sergi/go-diff. Besides, BurndownAnalysis involves File and RBTree. These are low level data structures which enable incremental blaming. File carries an instance of RBTree and the current line burndown state. RBTree implements the red-black balanced binary tree and is based on https://github.com/yasushi-saito/rbtree. Coupling stats are supposed to be further processed rather than observed directly. labours.py uses Swivel embeddings and visualises them in Tensorflow Projector. Shotness analysis as well as other UAST-featured items relies on [Babelfish](https://doc.bblf.sh) and requires the server to be running.
Package hercules contains the functions which are needed to gather various statistics from a Git repository. The analysis is expressed in a form of the tree: there are nodes - "pipeline items" - which require some other nodes to be executed prior to selves and in turn provide the data for dependent nodes. There are several service items which do not produce any useful statistics but rather provide the requirements for other items. The top-level items include: - BurndownAnalysis - line burndown statistics for project, files and developers. - CouplesAnalysis - coupling statistics for files and developers. - ShotnessAnalysis - structural hotness and couples, by any Babelfish UAST XPath (functions by default). The typical API usage is to initialize the Pipeline class: Then add the required analysis: This call will add all the needed intermediate pipeline items. Then link and execute the analysis tree: Finally extract the result: The actual usage example is cmd/hercules/root.go - the command line tool's code. Hercules depends heavily on https://github.com/src-d/go-git and leverages the diff algorithm through https://github.com/sergi/go-diff. Besides, BurndownAnalysis involves File and RBTree. These are low level data structures which enable incremental blaming. File carries an instance of RBTree and the current line burndown state. RBTree implements the red-black balanced binary tree and is based on https://github.com/yasushi-saito/rbtree. Coupling stats are supposed to be further processed rather than observed directly. labours.py uses Swivel embeddings and visualises them in Tensorflow Projector. Shotness analysis as well as other UAST-featured items relies on [Babelfish](https://doc.bblf.sh) and requires the server to be running.
Package hercules contains the functions which are needed to gather various statistics from a Git repository. The analysis is expressed in a form of the tree: there are nodes - "pipeline items" - which require some other nodes to be executed prior to selves and in turn provide the data for dependent nodes. There are several service items which do not produce any useful statistics but rather provide the requirements for other items. The top-level items include: - BurndownAnalysis - line burndown statistics for project, files and developers. - CouplesAnalysis - coupling statistics for files and developers. - ShotnessAnalysis - structural hotness and couples, by any Babelfish UAST XPath (functions by default). The typical API usage is to initialize the Pipeline class: Then add the required analysis: This call will add all the needed intermediate pipeline items. Then link and execute the analysis tree: Finally extract the result: The actual usage example is cmd/hercules/root.go - the command line tool's code. You can provide additional options via `facts` on initialization. For example, to provide your own logger, enable people-tracking, and set a custom tick size: Hercules depends heavily on https://github.com/src-d/go-git and leverages the diff algorithm through https://github.com/sergi/go-diff. Besides, BurndownAnalysis involves File and RBTree. These are low level data structures which enable incremental blaming. File carries an instance of RBTree and the current line burndown state. RBTree implements the red-black balanced binary tree and is based on https://github.com/yasushi-saito/rbtree. Coupling stats are supposed to be further processed rather than observed directly. labours.py uses Swivel embeddings and visualises them in Tensorflow Projector. Shotness analysis as well as other UAST-featured items relies on [Babelfish](https://doc.bblf.sh) and requires the server to be running.
Package otelfiber instruments the github.com/gofiber/fiber package. (https://github.com/gofiber/fiber). Currently, only the routing of a received message can be instrumented. To do so, use the Middleware function.
ghstat - statistical multi-criteria decision-making comparator for Github's projects.