Fri 8 Sep 2023 11:15 - 11:45 at f142 - NL Processing Chair(s): Chetan Arora

Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.

Fri 8 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:45 - 12:15
NL ProcessingJournal-First / Industrial Innovation Papers at f142
Chair(s): Chetan Arora Monash University
RClassify: Combining NLP and ML to Classify Rules from Requirements Specifications Documents
Industrial Innovation Papers
A: Asha Rajbhoj TCS Research, A: Padmalata Nistala , A: Ajim Pathan TCS Research, A: Piyush Kulkarni TCS Research, A: Vinay Kulkarni Tata Consultancy Services Research
Requirements Classification for Smart Allocation: A Case Study in the Railway Industry
Industrial Innovation Papers
A: Sarmad Bashir RISE Research Institutes of Sweden, A: Muhammad Abbas RISE Research Institutes of Sweden AB, A: Alessio Ferrari CNR-ISTI, A: Mehrdad Saadatmand RISE Research Institutes of Sweden, A: Pernilla Lindberg Alstom
DOI Pre-print
Enhanced Abbreviation-Expansion Pair Detection for Glossary Term Extraction
A: Hussein Hasso Fraunhofer FKIE, A: Katharina Großer University of Koblenz, A: Iliass Aymaz Fraunhofer FKIE, A: Hanna Geppert Fraunhofer FKIE, A: Jan Jürjens University of Koblenz-Landau
Link to publication DOI Pre-print File Attached