Automatically detecting the conflicts between software requirements based on finer semantic analysis

• URL: http://arxiv.org/abs/2103.02255v1
• Date: Wed, 3 Mar 2021 08:39:53 GMT
• Title: Automatically detecting the conflicts between software requirements based on finer semantic analysis
• Abstract summary: We propose an approach Finer Semantic Analysis-based Requirements Conflict Detector (FSARC) to automatically detecting the conflicts between the given natural language functional requirements. We build a harmonized semantic meta-model of functional requirements with the form of eight-tuple. And we define seven types of conflicts as long as their detecting rules on the ground of their text pattern and semantical dependency.
• Score: 8.035669538927623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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