Agile and Scrum are great in terms of delivering software in an iterative and predictable fashion, promoting development that is aligned towards the expected outcome by accepting early feedback. That said, the quality and longevity of the application is often driven by sound engineering practices put in place during the course of development. This also means that while burn down charts, velocity, and story level progress measures have their value in providing a sense of completion, unless some process guidance is established to measure engineering success during the application lifecycle, it is difficult to be certain about the behaviour of the application during go live and thereafter. An unpredictable behaviour does not instill confidence in using the product, ultimately spoiling the reputation of the project team engaged in delivering a quality software. The question is then, what matrices are key towards reporting and measuring engineering work?
LOC vs LOPC
Traditionally, raw lines of code (LOC) was used as a measure to qualify engineering productivity, however the approach is significantly flawed. A seasoned programmer can produce the same outcome in significantly fewer lines of code in comparison with a newbie. It is important for the code to stick around. A good measure, in that case, would be lines of productive code (LOPC). Measuring LOPC over a timeline gives you a good idea about individual developer productivity during the course of development and will empower you to make decisions in optimizing the team composition. For example, you can plot every 100 LOPC checked-in by a programmer over a time graph and it will help you predict behaviour. A developer shows significant improvement if he or she is taking, on average, less time to deliver 100 LOPC since beginning the program.
Code Churn is another critical factor in measuring engineering success. Refactoring causes code churn. While the team may be producing lots of lines of code in producing software, however the gap between LOC and LOPC is increasing — showing significant churn. This analysis helps nudge a programmer who is not putting sufficient effort towards writing quality code the first time around. Over a period of time, as team members get a better understanding of the requirements, the churn should reduce. If that is not the case it is an indicator that you need to make changes in your team composition.
Agile programming, quality assurance strategies, Scrum, application life cycle management, code quality, agile practices, churn