The article: Development governance for software management
Summary:
Governance is an interesting concept that is different from management. Governance is the act of exerting management control to guide development practices to compliance, while management normally consist of overseeing inward personnel and operations and a set of outward-facing responsibilities: planning, budgeting and forecasting etc.
To measure that to governance, the article use the term key performance indicator(KPI). The two KPIs mainly discuss about are volatility KPI and volume KPI -- both are process KPIs. The volatility is good to measure and predict development processes. Monitoring the volume will help to forecast project to adjust resource distribution; assure quality; and measure the efficacy of a software design in programming aspect. It also talked a little bit about work-product KPIs such as coding guidelines and complexity.
Relevance to my research:
In Hackystat, volatility is measured as Churn and volume is measured as FileMetric. Hackystat also provides other measures like coupling, coverage, build, test, commits and code issue. They are now equal. But from the enlightening from the article, I realize they should be group into two: process measures and work-product measures. The formers show the performance of the develop team while laters show the quality of the product. The understanding of these two groups are different thus research to them should be somehow differentiated.
Process measures will include:
Work-product measures will include:
An important idea from the article is that all these measures have to be monitor over time to give significant meaning. Even when talking about volume, what make sense is the change of the volume over time -- LOC increase relatively slow indicates efficacy design and bloated code usually brings negative impacts. The idea of looking at these measures is to look into their trend, in order to predict the processes. This match our idea in Project Portfolio. We show not only the current state of each measure, but also the historical trends. And we estimate projects performance from their analysis trend as well.
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