Extracting Software Requirements from Unstructured Documents
This addresses the tedious and error-prone task of requirements identification for software developers, though it is incremental as it applies an existing method to new data.
The paper tackled the problem of automating software requirements extraction from unstructured documents by fine-tuning BERT on a manually annotated dataset, achieving high precision and recall compared to baselines like fastText and ELMo.
Requirements identification in textual documents or extraction is a tedious and error prone task that many researchers suggest automating. We manually annotated the PURE dataset and thus created a new one containing both requirements and non-requirements. Using this dataset, we fine-tuned the BERT model and compare the results with several baselines such as fastText and ELMo. In order to evaluate the model on semantically more complex documents we compare the PURE dataset results with experiments on Request For Information (RFI) documents. The RFIs often include software requirements, but in a less standardized way. The fine-tuned BERT showed promising results on PURE dataset on the binary sentence classification task. Comparing with previous and recent studies dealing with constrained inputs, our approach demonstrates high performance in terms of precision and recall metrics, while being agnostic to the unstructured textual input.