Why does tracking matter, what could we gain from tracking, and what could we do with information? Tracking has been a way that our ancestors have been able to determine the best course of actions like where to go, and gave more insight into the action to be better. Without tracking, many things that are predictable would be left to chance. Imagine if we were not able to keep records and be able to track down food sources; would we have stood a chance against the elements? Ultimately, tracking provides us a track record of past things done in order for better efficiency. Why waste energy or time on things that are easily predictable and trackable? Therefore, tracking has been a cornerstone of the most important way we are able to determine what actions require better insight into in order to be more efficient.
In the context of programming, this has always been true. The problem was more about what needed to be more efficient. In the case of programming, it provides insight about files, code, and coding that require more resources or less. Without the tracking system, we cannot identify what would need more resources, therefore the purpose of tracking is not sufficient in answering this issue.
In the case of our project, we sought to track the estimation of effort in order to determine what functions required more effort and how we managed to execute the task. Without this estimation, we couldn’t be able to even begin to manage our time wisely. Therefore, the first thing that we attempted to do was first identify all the tasks that we needed to do in order to execute the functions of what we determined. Once our team was able to break down the problem, we decided to coordinate several tasks and assign them estimation of efforts.
This function was set up through GitHub’s tracking estimation, embedded in the projects tab. In it allowed us to create issues, assign them to people, estimate the time needed to execute, and then determine who implemented it and who executed it. This robust tracking system ultimately provided us with a lot of the scope of the estimations, but for the actuality, we needed to track that ourselves. For example, the tracker function tracked things in times of hours. However, not all tasks required hours of work, but each task ultimately defined the entirety of the project. The estimation could be lower or higher in actuality, thus tracking actuality required us to time it ourselves. I used two tracker systems and then took the average of both inputs, one was through screen time of one application and the other was using my phone as a timer. These two data points allowed us to be more accurate about the time in actuality.
I reflect on my time working on the project with teammates and other fellow students. What I have come to realize is the effect tracking this information has for not just management purposes but resource as well. If we didn’t plan to track our progress or how we are executing these tasks, then we cannot find a reason to know whether we are moving in the right direction. In future iterations, I hope to be more deliberate about the requirements for reflecting, and also I would recommend that we continue to track the progress week by week. A review of our work shows that the majority of the hours that we work are immensely reflective of the final product.