Background: Most software effort estimation research focuses on methods that produce the most accurate models but very little focuses on methods of mapping those models to business needs.
Aim: Rather than focusing only on algorithm mining, placing the main spotlight on the learner, here we focus on techniques to apply the model in the real world as well. We propose an algorithm called IDEA which creates a dendrogram that can be used to base project parameters decisions to optimize results.
Method: IDEA is compared to 90 solo-methods on 20 datasets using median MRE values. We also show worked examples of how to apply IDEA’s results to project parameter decisions.
Results: By applying IDEA to software effort estimation datasets we generate dendrograms used to make project parameter choices. IDEA does better than most methods 15% of the time, it does just as good as any other method 30% of the time, 35% of the time it does as good as half and 20% of the time it’s in the middle.
Conclusion: IDEA is a linear-time algorithm which can be used effectively to facilitate project decisions and is comparable or better than at least 90 solo-methods tested on 20 effort datasets found in the literature.