Children’s lives are increasingly intertwined with AI systems, from recommender algorithms to generative models, raising concerns about potential impacts on children’s agency. Although supporting human agency, autonomy, and empowerment is a widely shared HCI goal, we lack clear definitions of these concepts in designing child-AI interaction. Through a review of 25 recent HCI studies, we find agency is rarely explicitly defined and its conceptualisation varies across something children innately possess and something to be developed. Our literature mapping shows that researchers observed agency through children’s planning and self-regulation, asserting control over AI systems, and critique and re-design of the status quo. Conditions reported by researchers that enable or constrain agency span epistemic conditions, interactional design, social context, and motivational orientation. Our review highlights gaps in research on designing for children’s agency. We advocate for conceptual clarity by drawing upon existing frameworks and highlight the importance of considering children’s agency through a relational lens.

In DevOps, the traceability of software artifacts is critical to the successful development and operation of project delivery to stakeholders. Before the introduction of end-to-end traceability in DevOps at a Data Analytics team at bp (BP plc), an international integrated energy company, the tracing of artifacts throughout a project life cycle was manual and time-consuming. This changed when traceability become more automated with end-to-end traceability capability as an offering on the platform. This paper reports on the ways of working and the experience of developers implementing DevOps for developing and putting in production a Javascript React web application, with a focus on traceability management of artifacts produced throughout the life cycle. This report highlights key opportunities and challenges in traceability management from the development stage to production.

Systematic literature reviews (SLRs) and systematic mapping studies (SMSs) are common studies in any discipline to describe and classify past works, and to inform a research field of potential new areas of investigation. This last task is typically achieved by observing gaps in past works, and hinting at the possibility of future research in those gaps. Using an NLP-driven methodology, this paper proposes a meta-analysis to extend current systematic methodologies of literature reviews and mapping studies. Our work leverages a Word2Vec model, pre-trained in the software engineering domain, and is combined with a time series analysis. Our aim is to forecast future trajectories of research outlined in systematic studies, rather than just describing them. Using the same dataset from our own previous mapping study, we were able to go beyond descriptively analysing the data that we gathered, or to barely 'guess' future directions. In this paper, we show how recent advancements in the field of our SMS, and the use of time series, enabled us to forecast future trends in the same field. Our proposed methodology sets a precedent for exploring the potential of language models coupled with time series in the context of systematically reviewing the literature.

A key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP). Our search strategy gathered a total of 96 papers in focus of our study, covering a period from 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers, and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability, and data availability. Based on these, we consolidated the following open challenges: representation similarity across artifacts, the effectiveness of NLP for traceability, and achieving scalable, adaptive, and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability and efforts in achieving interoperability and explainability in NLP models for traceability.