Haste Makes Waste When You Over-Staff to Achieve Schedule Compression
Schedule pressures often force development managers to seek strategies to compress project time scales. Typical strategies might include adding more people, investing in development tools, improving hardware, and improving development methods. Is it possible to see which of these strategies is most effective? In this research we concentrate on the application of people to achieve time compression.
Managers often resort to the people strategy because it's the most straightforward to implement. People can be easily redeployed and no major capital investment is required. But how much schedule compression is achieved by adding people? And are there any side effects?
QSM maintains a metrics database of over 4,000 completed, high-confidence projects. In this study we analyzed 3 separate data sets:
- Information systems completed after 1994
- Engineering systems completed after 1994
- Real-time embedded systems completed after 1994
For each set we separated the projects into small teams (fewer than 5 people ) and large teams (more than 20 people). We then graphed the data showing schedule, effort and defects versus the size of the system that was built. This shows how much schedule compression was achieved by the larger teams as well as any insights into the side effects of cost and defects.
To illustrate the findings we show the data from the information systems data set. Both the engineering and real-time embedded sets showed similar results - in fact, they were more extreme.
In this figure we show peak staffing plotted at the size of the developed source lines of code. The red squares are projects that used 5 or fewer people; the blue squares are projects that used 20 or more people. The line through the data shows the average staff at a given size. For comparison purposes the graph shows the average staffing at 100,000 source lines of code with team sizes of 4 and 32 people.
The second figure shows the effort/cost differences experienced with the different strategies. For a typical project of 100,000 source lines of code, a 32-person team on average would have used 178 person-months of effort ($2.1 million at $12,000 per person-month). The 4-person team would have used 24.5 person-months ($294,000 @ $12,000 per person-month). The difference between the strategies is approximately $1.84 million.
How much schedule compression was achieved? Not much! The difference between the large team and the small team approaches for the average project of 100,000 source lines of code is approximately 1 calendar week. We do note that there is more variability in the small team data set, but w