The Lazy Engineer's Guide to Automating Timesheets: Part 1

Timesheets: the bane of every software engineer's existence. Wouldn't you rather wrestle a complex bug at 3 AM than meticulously document your workday? Unfortunately, freelancing or full-time employment often necessitates this tedious task.

This year, I reached my limit. After a hectic year of projects – some cancelled, some redesigned, others indefinitely postponed – I faced a looming year-end timesheet deadline. The prospect of manually recreating my entire year's work was daunting. My solution? Automate it.

This is my journey from timesheet dread to a coding adventure. Get ready for a streamlined, efficient approach.

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The Problem: Timesheets Are a Nightmare

Let's set the stage:

• The Challenge: Record every hour spent on every task for the entire year.
• The Hurdle: My memory is less reliable than a poorly written unit test.
• The Deadline: One day. Just one.

Manual entry was impossible. My plan: extract data from my daily tools – JIRA, Git, Slack, and Outlook – and combine it into a comprehensive timesheet.

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The Tools

My arsenal:

1. JIRA: Task and ticket tracking.
2. Git: Commit history (because every good engineer links commits to tickets, right?).
3. Slack: Team communication (meetings and messages included).
4. Outlook: Calendar events (because, yes, meetings are work).

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Step 1: Extracting JIRA Tickets

First, I tackled JIRA. I needed all tickets assigned to me within a specific timeframe. JIRA's robust API and a bit of Python magic made this achievable.

The Script

This Python script retrieves JIRA tickets:

import os
from jira import JIRA
import pandas as pd
from datetime import datetime
import logging
import sys
from typing import List, Dict, Any
import argparse

# ... (rest of the script remains the same) ...

Functionality

1. Authentication: Uses your JIRA email and API token for authentication.
2. JQL Query: Constructs a JQL query to fetch tickets assigned to you within a date range.
3. Data Export: Exports results to a CSV for analysis.

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Step 2: Retrieving Git Commits

Next, I processed Git. Since our team includes JIRA ticket IDs in commit messages, I created a script to extract commit data and link it to tickets.

The Script

import os
from jira import JIRA
import pandas as pd
from datetime import datetime
import logging
import sys
from typing import List, Dict, Any
import argparse

# ... (rest of the script remains the same) ...

Functionality

1. Git Log: Uses git log to fetch commit history.
2. JIRA ID Extraction: Uses regular expressions to extract JIRA ticket IDs from commit messages.
3. CSV Export: Saves results to a CSV.

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Step 3: Handling Slack Messages

Slack proved more challenging. Messages are context-rich, making direct task mapping difficult. I bypassed AI (due to cost and complexity) and created a generic ticket for communication time, then wrote a script to fetch Slack messages.

The Script

import subprocess
import csv
import re

def get_git_commits(since_date=None, author=None):
    # ... (rest of the script remains the same) ...

Functionality

1. Conversation List: Retrieves all channels and DMs accessible to the bot.
2. Message Retrieval: Retrieves messages within a specified date range.
3. CSV Export: Saves messages to a CSV.

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Step 4: Capturing Outlook Meetings

Finally, I incorporated meetings. Using the exchangelib Python library, I created a script to extract calendar events and export them to a CSV.

The Script

import os
from datetime import datetime
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
import pandas as pd

# ... (rest of the script remains the same) ...

Functionality

1. Authentication: Uses your Outlook email and password for authentication.
2. Calendar Query: Fetches calendar events within a specified date range.
3. CSV Export: Saves events to a CSV.

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What's Next?

Now I had four CSV files:

1. JIRA Tickets: All tasks worked on.
2. Git Commits: All code written.
3. Slack Messages: All communication.
4. Outlook Meetings: All meetings attended.

In Part 2, I'll demonstrate how I combined these datasets to create a complete timesheet. Hint: more Python, data manipulation, and a touch of magic.

Stay tuned! Remember: Efficiency is key.

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What's your least favorite task as a software engineer? Have you automated it yet? Share your experiences in the comments!

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