Automatic Estimation Techniques are Useful?

Best practices for software effort estimation can include the use of automatic techniques to summarize past data. There exists a large and growing number of techniques. Which are useful? In this study, 158 techniques were applied to some COCOMO data. 154 158 = 97% of the variants explored below add little or nothing to a standard linear model (with simple column and row pruning). For example, learners that assume multiple linear models (such as model trees) or no parametric form at all (such as nearest neighbor estimation) perform relatively poorly. Also, elaborate searches did not add value to effort estimation. Exponential time searching for the best subsets of the columns performed no better than near-linear-time column pruning. It is possible that other techniques, not included in the 158 techniques studied here, might perform better. However, based on current evidence, this study concludes that when technique learners are used for effort estimation, a linear model with simple column and row pruning will suffice (at least, for COCOMO-style data).