Automated separation of crosscutting concerns: Earlier Automated identification and modularization of cross-cutting features at analysis phase

Abstract

Early aspect mining captures the concerns that can propagate to other artifacts in later stage. However, current approaches and tools required a self made input by following specific grammatical patterns to expose to the approach what the concern is. Moreover, requirements are mostly communicated between the stakeholders in form of features. However, the early aspect mining from the feature introduced the labor intensive task of creating feature model that is unable to support cross-cutting relations. There seems to be a tradeoff between the requirement abstraction and automaticity for aspect discovery at early analysis phase. In this paper, we present an enhanced form of aspect-oriented feature analysis (AOFA), which discovers meaningful concerns and feature interactions, then associates them to feature modules without disbursing automaticity. It takes publically available unstructured features as input then creates a knowledge base of domain by natural language processing and finally models each feature's dependencies by utilizing this domain knowledge and variability patterns. We evaluate our approach against early aspect miner tool and statistical method and found our approach to be optimal.