Background: Poor communication of requirements between clients and suppliers contributes to project overruns, in both software and infrastructure projects. Existing literature offers limited insights into the communication challenges at this interface.

Aim: Our research aim to explore the processes and associated challenges with requirements activities that include client-supplier interaction and communication.

Method: we study requirements validation, communication, and digital asset verification processes through two case studies in the road and railway sectors, involving interviews with ten experts across three companies.

Results: We identify 13 challenges, along with their causes and consequences, and suggest solution areas from existing literature.

Conclusion: Interestingly, the challenges in infrastructure projects mirror those found in software engineering, highlighting a need for further research to validate potential solutions.

Background: Traceability between software artifacts enhances the value of the information those artifacts contain, but only when the links themselves are reliable. Link quality is known to depend on explicit factors such as the traced artifacts and the expertise of the practitioner who judges each connection. Other factors, however, remain largely unexplored. We contend that one of these factors is the set of auxiliary artifacts---artifacts that are produced and/or used during the tracing process, yet are neither the source nor target artifacts. Because such auxiliary artifacts can subtly steer how links are created and validated, they merit a literature survey to identify these artifacts and further investigate them.

Objective: We identify and map auxiliary artifacts used in requirements tracing, which could be additional factors that affect the quality of the trace links.

Method: We conducted a systematic mapping study on auxiliary artifacts in requirements traceability.

Results: We found 110 studies in which auxiliary artifacts are used in requirements tracing, and identified 49 auxiliary artifacts, and 13 usage scenarios.

Conclusion: This study provides a systematic mapping of auxiliary artifacts in requirement tracing, including their usage, origin, type and tool support. The use of auxiliary artifacts in requirements tracing seems to be the norm, thus, these artifacts should be studied in depth to identify how they effect the quality of traced links.

Context and motivation:

Classification aids software development activities by organizing requirements in classes for easier access and retrieval. The majority of requirements classification research has, so far, focused on binary or multi-class classification.

Question/problem:

Multi-label classification with large taxonomies could aid requirements traceability but is prohibitively costly with supervised training. Hence, we investigate zero-short learning to evaluate the feasibility of multi-label requirements classification with large taxonomies.

Principal ideas/results:

We associated, together with domain experts from the industry, 129 requirements with 769 labels from taxonomies ranging between 250 and 1183 classes. Then, we conducted a controlled experiment to study the impact of the type of classifier, the hierarchy, and the structural characteristics of taxonomies on the classification performance. The results show that: (1) The sentence-based classifier had a significantly higher recall compared to the word-based classifier; however, the precision and F1-score did not improve significantly. (2) The hierarchical classification strategy did not always improve the performance of requirements classification. (3) The total and leaf nodes of the taxonomies have a strong negative correlation with the recall of the hierarchical sentence-based classifier.

Contribution:

We investigate the problem of multi-label requirements classification with large taxonomies, illustrate a systematic process to create a ground truth involving industry participants, and provide an analysis of different classification pipelines using zero-shot learning. © 2024 IEEE.

Background:

Multi-label requirements classification is an inherently challenging task, especially when dealing with numerous classes at varying levels of abstraction. The task becomes even more difficult when a limited number of requirements is available to train a supervised classifier.  Zero-shot learning does not require training data and can potentially address this problem.

Objective:

This paper investigates the performance of zero-shot classifiers on a multi-label industrial dataset. The study focuses on classifying requirements according to a hierarchical taxonomy designed to support requirements tracing.

Method:

We compare multiple variants of zero-shot classifiers using different embeddings, including 9 language models (LMs) with a reduced number of parameters (up to 3B), e.g., BERT, and 5 large LMs (LLMs) with a large number of parameters (up to 70B), e.g., Llama. Our ground truth includes 377 requirements and 1968 labels from 6 output spaces. For the evaluation, we adopt traditional metrics, i.e., precision, recall, $F_1$, and $F_\beta$, as well as a novel label distance metric $D_n$. This aims to better capture the classification's hierarchical nature and to provide a more nuanced evaluation of how far the results are from the ground truth.

Results:

1) The top-performing model on 5 out of 6 output spaces is T5-xl, with maximum  $F_\beta = 0.78$ and $D_n = 0.04$, while BERT base outperformed the other models in one case, with maximum $F_\beta = 0.83$ and $D_n = 0.04$. 2) LMs with smaller parameter size produce the best classification results compared to LLMs. Thus, addressing the problem in practice is feasible as limited computing power is needed. 3) The model architecture (autoencoding, autoregression, and sentence-to-sentence) significantly affects the classifier's performance.

Contribution:

We conclude that using zero-shot learning for multi-label requirements classification offers promising results. We also present a novel metric that can be used to select the top-performing model for this problem.

Context: Traceability is a key quality attribute of artifacts that are used in knowledge-intensive tasks and supports software engineers in producing higher-quality software. Despite its clear benefits, traceability is often neglected in practice due to challenges such as granularity of traces, lack of a common artifact structure, and unclear responsibility. The Taxonomic Trace Links (TTL) approach connects source and target artifacts through a domain-specific taxonomy, aiming to address these common traceability challenges.

Objective: In this study, we empirically evaluate TTL in an industrial setting to identify its strengths and weaknesses for real-world adoption.

Method: We conducted a mixed-methods study at Ericsson involving one of its software products. Quantitative and qualitative data were collected across two traceability use cases. We established trace links between 463 business use cases, 64 test cases, and 277 ISO-standard requirements. Additionally, we held three focus group sessions with practitioners.

Results: We identified two practically relevant scenarios where traceability is required and evaluated TTL in each. Overall, practitioners found TTL to be a useful solution for identifying trace links with reasonable effort. However, developing a domain-specific taxonomy and managing heterogeneous artifact structures were noted as significant challenges.

Conclusion: TTL is a promising approach that can be adopted in practice and enables traceability use cases. However, TTL are not a replacement for traditional trace links, but complementary to enable more traceability use cases, and encourage early trace links creation.

Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis. Despite its clear benefits, the practical implementation of traceability poses significant challenges, leading to a reduced focus on the creation and maintenance of trace links. We propose a new approach --- Taxonomic Trace Links (TTL) --- which rethinks traceability and its benefits. With TTL, trace links are created indirectly through a domain-specific taxonomy, a simplified version of a domain model. TTL has the potential to address key traceability challenges, such as the granularity of trace links, the lack of a common data structure among software development artifacts, and unclear responsibility for traceability. We explain how TTL addresses these challenges and perform an initial validation with practitioners. We identified six challenges associated with TTL implementation that need to be addressed. Finally, we propose a research roadmap to further develop and evaluate the technical solution of TTL. TTL appears to be particularly feasible in practice where a domain taxonomy is already established.