Towards Extracting Software Requirements from App Reviews using Seq2seq Framework

TL;DR

This paper introduces a novel Seq2seq-based framework for extracting software requirements from app reviews, addressing informal language and irrelevant info, and demonstrating superior performance over existing methods.

Contribution

The paper reformulates requirements extraction as a NER task using a Seq2seq model with attention, GloVe embeddings, and CRF, improving accuracy on large review datasets.

Findings

Outperformed state-of-the-art methods with F1 score of 0.96 on large dataset.

Achieved comparable performance with existing methods on smaller dataset.

Effectively handled informal language and irrelevant information in reviews.

Abstract

Mobile app reviews are a large-scale data source for software improvements. A key task in this context is effectively extracting requirements from app reviews to analyze the users' needs and support the software's evolution. Recent studies show that existing methods fail at this task since app reviews usually contain informal language, grammatical and spelling errors, and a large amount of irrelevant information that might not have direct practical value for developers. To address this, we propose a novel reformulation of requirements extraction as a Named Entity Recognition (NER) task based on the sequence-to-sequence (Seq2seq) generation approach. With this aim, we propose a Seq2seq framework, incorporating a BiLSTM encoder and an LSTM decoder, enhanced with a self-attention mechanism, GloVe embeddings, and a CRF model. We evaluated our framework on two datasets: a manually annotated set of 1,000 reviews (Dataset 1) and a crowdsourced set of 23,816 reviews (Dataset 2). The quantitative evaluation of our framework showed that it outperformed existing state-of-the-art methods with an F1 score of 0.96 on Dataset 2, and achieved comparable performance on Dataset 1 with an F1 score of 0.47.