By Jonathan Alexander
How are you going to support your software program workforce enhance? This concise e-book introduces codermetrics, a transparent and aim solution to establish, research, and speak about the successes and screw ups of software program engineers-not as a part of a functionality evaluate, yet on the way to make the staff a extra cohesive and efficient unit. skilled staff builder Jonathan Alexander explains how codermetrics is helping groups comprehend precisely what happened in the course of a venture, and allows each one coder to target particular advancements. Alexander provides various easy and intricate codermetrics, and teaches you the way to create your individual.
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Extra resources for Codermetrics: Analytics for Improving Software Teams
For the data obtained from systems used to track coder work, such as project or bug tracking systems, you may choose to obtain data weekly, and you will want to obtain data for individual coders, teams, and product areas. For the data obtained from business systems outside development, such as a customer support system, it may be fine to get monthly summaries, but you will still want data broken down by product areas. com, Microsoft Dynamics, RightNow, Zendesk Useful data: support contact counts, support case counts, case severity Granularity: Weekly by product area Your software product (instrumented for appropriate monitoring) Useful data: user activations, logins, feature usage, user errors, performance Granularity: weekly by product As you obtain data, you will need to decide how to store it.
Larger teams are less efficient. • Difficult tasks will be done better by more experienced coders. Whether you assume the above statements are true or false, without good data and proof, you don’t really know. Metrics give you the data to examine your assumptions. Identifying the key assumptions that you would like to investigate more closely, especially those that drive key decisions in your daily work, is also a great way to determine what kind of metrics you should gather and use. For example, if you want to examine the assumption that “difficult tasks will be done better by more experienced coders,” you need to gather metrics on the complexity of tasks, the experience level of coders, and the relative quality of completed work.
Most of those issues had been fixed by that point, but still the team took note for future consideration. For the first time, we also started looking at individual coder metrics. We looked at complexity of work done in sprints, including feature development and product bug fixes. We looked at interruptions that each coder dealt with, and we looked at how many different parts of code everyone worked on. ” We also looked at these metrics aggregated by team. We discussed how these might correlate to team goals, team success, product success, and company goals.