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.

Abstract Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.

Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm's efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

Software development industry is growing rapidly and so are the time and budget constraints getting stringent. After Scrum, the widely adopted agile method, agile practitioners are now shifting towards Kanban due to its effective communication facilitation, transparency and limited work in progress traits. Since, the industry is in transition from scrum to Kanban therefore we don't find many empirical studies yielding results of adopting Kanban. Therefore, in this study we aim to explore more on Kanban teams. Mainly, we aim to find the impact of Kanban team's communication patterns on their iteration performance and quality. The findings revealed that the centralization communication patterns have negative impact on iteration performance and quality of a project. However, small world communication pattern has positive impact on iteration performance and quality of a project.