FACIA: A Fully Automatic Change Impact Analysis Method for Large Scale Requirements

Wan-yu CHEN, Hong-hui CHEN, Tao CHEN, Fei CAI

Abstract


The blooming change of requirements poses a challenge for change impact analysis (CIA), especially in large scale software development. Existing CIA techniques exploiting manual analysis and automatic methods suffer from high cost, low accuracy and expert dependence in current industrial practice. Therefore, this paper proposes a fully automatic change impact analysis (FACIA) method to overcome the aforementioned shortcomings. We consider the requirement changes generally happen as the form of phrases. The impact units for the changed phrases may contain the changed phrases (CP1), the co-occurrence phrases of CP1(CP2), the tokens of CP1 in the changed requirement, the similar phrases of CP1 and the similar phrases of CP2 in the whole requirements. Then five algorithms are designed based on the combinations of those impact units, so as to get a more precise change propagation and generate the lists of change impact automatically. We conduct extensive evaluations for the proposed approach with two different industrial data sets. The results show that compared with the method of expert depended, our approach can get a reliable sorted list for CIA more quickly with lower cost.

Keywords


Blooming changes, Change impact analysis, Impact units, Fully automatic technology


DOI
10.12783/dtetr/amma2017/13398

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