During our work on the Campus Plate Mate project, our team practiced effort estimation and tracked our time spent on issues through GitHub. Although our estimates were often inaccurate—sometimes significantly off—the process of making them still provided unexpected benefits. It helped us develop a more mindful approach to planning, time management, and team coordination throughout the semester.
I made my effort estimates based on a combination of previous experience, the complexity of the issue, and how familiar I was with the tools or concepts involved. If a task seemed straightforward or resembled something I had done before, I would give it a shorter estimate. For tasks that involved learning new frameworks, working with unfamiliar code, or debugging, I typically allowed more time. However, I often underestimated how long tasks would actually take—especially when unexpected challenges arose. These estimates frequently failed to account for hidden complications, such as deployment issues on Vercel or unexpected errors during Prisma migrations. Over time, I learned that even seemingly small tasks could expand due to these unpredictable roadblocks.
Yes, even though my estimates were often inaccurate, they were still helpful. Making estimates in advance pushed me to break down tasks and think critically about their scope and potential challenges. It encouraged more intentional planning and helped me prioritize my time more effectively. On a team level, having rough time estimates gave us a starting point for distributing tasks and managing our collective workload. This was especially valuable since many of us were balancing the project alongside jobs, other classes, and personal responsibilities. Even when the estimates were off, they provided structure and direction that made our work more manageable.
Tracking actual effort was beneficial because it gave me a clearer picture of how I was using my time and which tasks were more demanding than expected. It helped identify where I underestimated complexity and where I could improve my efficiency. One downside was the extra effort required to consistently log my time, which sometimes felt tedious. However, the insights I gained outweighed the inconvenience.
I tracked my actual effort using GitHub’s team project board, where we created a table called Effort Estimator that listed all the issues along with their assignees. For each issue, we would estimate how much time we expected it to take. When working on a task, I would note the time I started, any breaks I took, and the time I resumed, continuing this until the issue was completed. For the most part, my tracking was fairly accurate. However, some tasks—especially those involving long debugging sessions—caused me to lose track of time. Smaller interruptions and occasional multitasking also affected the precision, but overall, the process gave me a useful overview of how I was spending my time and which tasks were taking longer than expected.
The overhead from tracking my effort was minimal and didn’t significantly inhibit my ability to work on the project. It only took a few seconds to jot down start and stop times or update the Effort Estimator table on GitHub. While it occasionally interrupted my flow—especially if I was deep in debugging or switching between tasks—it wasn’t a major distraction. In fact, the small time investment often paid off by helping me stay more organized and aware of how I was spending my time. Overall, the benefits of tracking effort outweighed the slight inconvenience.
ChatGPT was used to refine the grammar and the clarity of my writing, however, all ideas and contents are entirely my own.