michaeldorner

curl -o response_1.txt -k "https://android-review.googlesource.com/changes/?q=change:I78c787fd5dd09fc7700f3093341532fe23f20eb8&o=DETAILED_LABELS" 
curl -o response_2.txt -k "https://android-review.googlesource.com/changes/I78c787fd5dd09fc7700f3093341532fe23f20eb8/detail"

michaeldorner

Pervasion of and Motivation behind Code Review

Question 1: Do you review code as part of your daily work?

• Rationale: We want to understand why developers review code. Our research indicated, that some just do not or if they do, they are forced to do so, but do not see any value in reviewing code.
• Answers: Single choice with
  • No.
  • Yes, because I was told to do so.
  • Yes, because I see value in code review.

michaeldorner

#!/bin/bash

# Texpad 1.7 Build script to build with latexmk

tempdir="$(dirname "${TEXPAD_ROOTFILE}")/.texpadtmp";

latexmk -pdf -interaction=nonstopmode -outdir="${tempdir}" -file-line-error -synctex=1 "${TEXPAD_ROOTFILE}";
#mv "${tempdir}/$(basename "${TEXPAD_ROOTFILE}")" .

michaeldorner

Onboarding

Administration

• Apply for Personnummer (via yourself)
• Get employee card (via reception)

Office

• Design and order business cards (via Svetlana)

michaeldorner

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io/ioutil"
	"net/http"
	"os"
	"time"

michaeldorner

def diffuse_information(G: nx.MultiGraph, seed: list, beta: float=1.0, case='best_case'):       
    informed_nodes = {n: None for n in seed}
    changed = True

    while changed:
        for u, v, dt in G.edges(nbunch=informed_nodes, data='diffusion_time'):
            changed = False
            if informed_nodes[u] == None or informed_nodes[u] < dt[case]: # shall we 
                if random.random() < beta:
                    if v not in informed_nodes or dt[case] < informed_nodes[v]:

michaeldorner

import requests
import json
from tqdm.notebook import tqdm
import time
import multiprocessing as mp
import datetime
import pandas as pd
import itertools

def convert_gerrit_timestamp_to_datetime(s):

michaeldorner

import networkx as nx

def diffuse_information(G: nx.MultiGraph, seed: list, case='best_case'):       
    informed_nodes = {n: None for n in seed}
    needs_update = True

    while needs_update:
        needs_update = False
        for u, v, dt in G.edges(nbunch=informed_nodes, data='diffusion_time'):
            if u in informed_nodes:

michaeldorner

\begin{figure}[!htp] \centering
\begin{tikzpicture}

\def \radius {3cm}
\def \margin {10} % margin in angles, depends on the radius 
\def \n {6}

\draw[color=white, name path=c] (0:\radius) arc (0:360:\radius);

\node[draw, rounded corners, fill=white, name path=realitynode] at ({360/\n * 2}:\radius) (reality) {Reality of interest}; 

michaeldorner

import timeit
import random
import networkx as nx

# from https://networkx.org/documentation/stable/_modules/networkx/algorithms/components/connected.html#connected_components
def _plain_bfs(G, source):
    """A fast BFS node generator"""
    G_adj = G.adj
    seen = set()
    nextlevel = {source}