Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification

TL;DR

This paper introduces LOGION, a local search algorithm that efficiently identifies boundary conditions in goal-oriented requirements engineering by leveraging structural similarities, resulting in more comprehensive and general BCs than existing methods.

Contribution

The paper presents a novel local search algorithm, LOGION, that exploits structural similarity of boundary conditions to improve the efficiency and coverage of goal conflict identification.

Findings

LOGION finds an order of magnitude more BCs than existing approaches.

LOGION identifies more general BCs due to larger BC set.

Experimental results confirm the effectiveness of structural similarity exploitation.

Abstract

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which make the goals diverge within the domain, called boundary conditions (BCs). However, the existing approaches for goal conflict identification fail to find sufficient BCs and general BCs which cover more combinations of circumstances. From the BCs found by these existing approaches, we have observed an interesting phenomenon that there are some pairs of BCs are similar in formula structure, which occurs frequently in the experimental cases. In other words, once a BC is found, a new BC may be discovered quickly by slightly changing the former. It inspires us to develop a local search algorithm named LOGION to find BCs, in which the structural similarity is captured by the neighborhood relation of formulae. Based on structural similarity, LOGION can find a lot of BCs in a short time. Moreover, due to the large number of BCs identified, it potentially selects more general BCs from them. By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs. We also compare our algorithm against the two state-of-the-art approaches. The experimental results show that LOGION produces one order of magnitude more BCs than the state-of-the-art approaches and confirm that LOGION finds out more general BCs thanks to a large number of BCs.