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.

中文翻译：

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边界条件的结构相似性和用于目标冲突识别的高效局部搜索算法

在面向目标的需求工程中，目标冲突识别对于需求分析至关重要。该任务旨在找到使目标在领域内发生分歧的可行情况，称为边界条件（BCs）。但是，用于目标冲突识别的现有方法无法找到足够的BC和涵盖更多情况组合的常规BC。从这些现有方法发现的BC中，我们观察到一个有趣的现象，即有些对BC的分子式结构相似，在实验情况下经常发生。换句话说，一旦找到了一个BC，就可以通过稍微改变前一个BC来迅速发现一个新的BC。它激发我们开发一种名为LOGION的本地搜索算法来查找BC，其中结构相似性是通过公式的邻域关系捕获的。基于结构相似性，LOGION可以在短时间内找到许多BC。此外，由于已识别的大量BC，它有可能从中选择更多的常规BC。通过对一组案例进行实验，我们表明LOGION有效地利用了BCs的结构相似性。我们还将我们的算法与两种最新方法进行了比较。实验结果表明，LOGION比最新方法产生的BC多一个数量级，并证实LOGION由于拥有大量BC而发现了更多的通用BC。它可能会从中选择更多的常规BC。通过对一组案例进行实验，我们表明LOGION有效地利用了BCs的结构相似性。我们还将我们的算法与两种最新方法进行了比较。实验结果表明，LOGION比最新方法产生的BC多一个数量级，并证实LOGION由于拥有大量BC而发现了更多的通用BC。它可能会从中选择更多的常规BC。通过对一组案例进行实验，我们表明LOGION有效地利用了BCs的结构相似性。我们还将我们的算法与两种最新方法进行了比较。实验结果表明，LOGION比最新方法产生的BC多一个数量级，并证实LOGION由于拥有大量BC而发现了更多的通用BC。