Software requirements are the driving force behind software development. As the cornerstone of the entire software lifecycle, the efficiency of crafting requirement specifications and the quality of these requirements significantly influence the duration of software development. Despite massive research on requirements elicitation, the reality is that requirements are often painstakingly crafted manually, word by word. This manual process is not only time-consuming but also prone to issues such as the misuse of terminology. To address these challenges, we introduce ReqCompletion, an approach designed to recommend the next token in real-time for given prefix of requirements description. ReqCompletion comprises two primary components. First, we have devised and integrated a knowledge-injection module into GPT-2—which stands as the largest available GPT model that allows for fine-tuning on specialized downstream tasks. This injection imbues GPT-2 with richer domain-specific knowledge, thus improving the relevance of the suggested tokens. Additionally, we employ a pointer network to optimize the recommendation quality by utilizing completed requirements as contextual support. Empirical evaluations using two public datasets demonstrate that ReqCompletion surpasses all baselines in performance (Recall@7 gains up to 65.87% than the second-best model). Furthermore, the effectiveness of its two pivotal design elements has been substantiated through rigorous ablation studies. The utility of our work has been evaluated preliminarily through a small user study.

Abstract Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation.