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.

---

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.

---

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).

---

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:

<code class="language-python">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) ...</code>

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.

---

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

<code class="language-python">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) ...</code>

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.

---

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

<code class="language-python">import subprocess
import csv
import re

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

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.

---

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

<code class="language-python">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) ...</code>

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.

---

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.

---

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

The above is the detailed content of The Lazy Engineer's Guide to Automating Timesheets: Part 1. For more information, please follow other related articles on the PHP Chinese website!