Learning to extract transaction function from requirements: an industrial case on financial software

In practice, it is very important to determine the size of a proposed software system yet to be built based on its requirements, i.e., early in the development life cycle. The most widely used approach for size estimation is Function Point Analysis (FPA). However, since FPA involves human judgment, the estimation results are some degree of subjective, and the process is labor and cost intensive. In this paper, we propose a novel approach to identify transaction functions from textual requirements automatically by leveraging a set of natural language processing techniques and machine learning models. We evaluate our approach on 1,864 requirements and 104,691 transaction functions taken from 36 financial projects from one banking industry. The results show that the contents of the suggested transaction functions by our approach are high in quality, with low perplexity value of 8.5 and high BLEU score of 34 on average. The types of suggested transaction functions can also be accurately classified, with overall accuracy of 0.99 on average. Our approach can provide reasonable suggestions that assist industrial practitioners to identify transaction functions faster and easier.