SANER 2018

2018 IEEE 25th International Conference on Software Analysis, Evolution, and Reengineering (SANER), March 20-23, 2018, Campobasso, Italy

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ERA Track
Extracting Features from Requirements: Achieving Accuracy and Automation with Neural Networks
Yang Li, Sandro Schulze, and Gunter Saake
(Otto von Guericke University Magdeburg, Germany)
Preprint
Abstract: Analyzing and extracting features and variability from different artifacts is an indispensable activity to support systematic integration of single software systems and Software Product Line (SPL). Beyond manually extracting variability, a variety of approaches, such as feature location in source code and feature extraction in requirements, has been proposed for automating the identification of features and their variation points. While requirements contain more complete variability information and provide traceability links to other artifacts, current techniques exhibit a lack of accuracy as well as a limited degree of automation. In this paper, we propose an unsupervised learning structure to overcome the abovementioned limitations. In particular, our technique consists of two steps: First, we apply Laplacian Eigenmaps, an unsupervised dimensionality reduction technique, to embed text requirements into compact binary codes. Second, requirements are transformed into a matrix representation by looking up a pre-trained word embedding. Then, the matrix is fed into CNN to learn linguistic characteristics of the requirements. Furthermore, we train CNN by matching the output of CNN with the pre-trained binary codes. Initial results show that accuracy is still limited, but that our approach allows to automate the entire process.

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Time stamp: 2020-04-05T04:59:08+02:00