Estimating software project effort by analogy based on linguistic values

Estimation models in software engineering are used to predict some important attributes of future entities such as software development effort, software reliability and programmers' productivity. Among these models, those estimating software effort have motivated considerable research in recent years. The prediction procedure used by these software-effort models can be based on a mathematical function or other techniques such as analogy based reasoning, neural networks, regression trees, and rule induction models. Estimation by analogy is one of the most attractive techniques in the software effort estimation field. However, the procedure used in estimation by analogy is not yet able to handle correctly linguistic values (categorical data) such as 'very low', 'low' and 'high'. We propose a new approach based on reasoning by analogy, fuzzy logic and linguistic quantifiers to estimate software project effort when it is described either by numerical or linguistic values; this approach is referred to as Fuzzy Analogy. This paper also presents an empirical validation of our approach based on the COCOMO'81 dataset.