  Context: Conflicts between software requirements bring uncertainties to
product development. Some great approaches have been proposed to identify these
conflicts. However, they usually require the software requirements represented
with specific templates and/or depend on other external source which is often
uneasy to build for lots of projects in practice. Objective: We aim to propose
an approach Finer Semantic Analysis-based Requirements Conflict Detector
(FSARC) to automatically detecting the conflicts between the given natural
language functional requirements by analyzing their finer semantic
compositions. Method: We build a harmonized semantic meta-model of functional
requirements with the form of eight-tuple. Then we propose algorithms to
automatically analyze the linguistic features of requirements and to annotate
the semantic elements for their semantic model construction. And we define
seven types of conflicts as long as their heuristic detecting rules on the
ground of their text pattern and semantical dependency. Finally, we design and
implement the algorithm for conflicts detection. Results: The experiment with
four requirement datasets illustrates that the recall of FSARC is nearly 100%
and the average precision is 83.88% on conflicts detection. Conclusion: We
provide a useful tool for detecting the conflicts between natural language
functional requirements to improve the quality of the final requirements set.
Besides, our approach is capable of transforming the natural language
functional requirements into eight semantic tuples, which is useful not only
the detection of the conflicts between requirements but also some other tasks
such as constructing the association between requirements and so on.

Software Engineering;Artificial Intelligence