Requirements workshop for software stakeholders is the major approach for collecting software requirements. Given the success of AI (Artificial Intelligence), especially NLP (Natural Language Processing) and the recent LLMs (Large Language Models), it is becoming increasingly popular for requirements engineers to use cloud-based AI services to automate the process of requirements documentation based on manually or AI generated meeting minutes either during or after the requirements workshop. However, using cloud-based AI services to process audio recordings of the requirements workshop may introduce many issues such as data privacy, high demand for computational resources and bandwidth, and large latency which make collaboration not efficient. To address these issues, we propose Edge4RE, a novel privacy-aware edge-cloud collaborative framework to support automated software requirements documentation from automated audio recordings during the requirements workshop. Specifically, with our solution, automated audio recordings are first transcribed and segmented into sentences on the local edge device. The edge device applies a lightweight Transformer based classifier fine-tuned using Low Rank Adaptation (LoRA) to identify requirement-related sentences. Afterwards, only requirement-related sentences are sent to the cloud-based LLM to generate the structured requirements documentation. Experimental results demonstrate that our framework can achieve high-quality requirement documentation based on automated audio recordings while reducing latency and bandwidth consumption with privacy protection.

Context: Personas are commonly employed in software projects to better understand end-users needs. Despite their frequent usage, there is a limited understanding of their practical application and effectiveness. Objective: This paper aims to investigate the current practices, methods, and challenges associated with using personas in software development. Methods: A two-step investigation was conducted, comprising interviews with 26 software developers, UI/UX designers, business analysts, and product managers, along with a survey of 203 practitioners. Results: The findings reveal variations in the frequency and effectiveness of personas across different software projects and IT companies. Additionally, the study highlights the challenges practitioners face when using personas and the reasons for not using them. Notably, the research shows that some human aspects (e.g., the needs of users with disabilities), often assumed to be a key feature of personas, are frequently not considered for various reasons in requirements engineering. Conclusions: The study provides actionable insights for practitioners to overcome challenges in using personas during the requirements engineering stages. Furthermore, it identifies areas for future research to enhance the effectiveness of personas in software development.

People who use software applications are different, including with significant cognitive differences such as neurodiversity. Capturing requirements for software that addresses these cognitive differences is hard for software engineers, especially when they do not have the same cognitive challenges. We wanted to explore the use of virtual reality (VR) in assisting software engineers to better understand the perspectives of the end user for the purpose of human-centric requirements elicitation, with a focus on users with attention-deficit/hyperactivity disorder (ADHD). We developed an immersive VR prototype using a virtual gym environment and fitness app as a concrete motivating example scenario. We carried out an evaluation of requirements identification for ADHD fitness app users by instructing participants to complete activities whilst under visual and auditory distractions similar to various documented symptoms of ADHD. Results indicated an increase in understanding the perspectives of someone with ADHD and an awareness of potential challenges with software not intentionally designed for ADHD users. Improved requirements for our target fitness app resulted that better take into account these diverse user needs.

Requirements engineering (RE) plays a crucial role in developing software systems by bridging the gap between stakeholders' needs and system specifications. However, effective communication and elicitation of stakeholder requirements can be challenging, as traditional RE methods often overlook emotional cues. This paper introduces a multi-modal emotion recognition platform (MEmoRE) to enhance the requirements engineering process by capturing and analyzing the emotional cues of stakeholders in real-time. MEmoRE leverages state-of-the-art emotion recognition techniques, integrating facial expression, vocal intonation, and textual sentiment analysis to comprehensively understand stakeholder emotions. This multimodal approach ensures the accurate and timely detection of emotional cues, enabling requirements engineers to tailor their elicitation strategies and improve overall communication with stakeholders. We further intend to employ our platform for later RE stages, such as requirements reviews and usability testing. By integrating multi-modal emotion recognition into requirements engineering, we aim to pave the way for more empathetic, effective, and successful software development processes. We performed a preliminary evaluation of our platform. This paper reports on the platform design, preliminary evaluation, and future development plan as an ongoing project.

Human emotion recognition has been widely used and intensively studied in many areas such as e-Commerce, online education, healthcare, human-computer interaction and recently human-centric software engineering (HCSE). HCSE investigates human factors in the entire software development lifecycle, and human emotion can be used in many scenarios such as requirement gathering and usability testing. However, even though existing studies have already shown the advantages of emotion recognition with multi-source data such as text, audio and video, the current research and practice in HCSE are primarily based on single-source data. In addition, emotion recognition in HCSE faces several challenges such as multiple participants, changing environments and real-time requirement. To tackle these challenges, this paper proposes Edge4Emotion, a novel edge computing-based multisource human emotion recognition platform for HCSE. Edge4Emotion takes the advantage of the edge computing paradigm to efficiently support the collection of multi-source data such as audio, video and physiological data from various IoT devices, and emotion recognition with both single- and multisource models. As an on-going project, this paper focuses on the platform design and the preliminary evaluation of the platform with representative emotion recognition applications. The platform will be further extended to include more multi-source learning models and serve as an open-source platform for the development and evaluation of multi-source emotion recognition models for HCSE.

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

Recognition of human behaviours including body motions and facial expressions plays a significant role in human-centric software engineering. However, due to the data and computation intensive nature of human behaviour recognition through video analytics, expensive powerful machines are often required, which could hinder the research and application in human-centric software engineering. To address such an issue, this paper proposes a cost-effective human behaviour recognition system named Edge4Real which can be easily deployed in an edge computing environment with commodity machines. Compared with existing centralised solutions, Edge4Real has three major advantages including cost-effectiveness, easy-to-use, and real-time. Specifically, Edge4Real adopts a distributed architecture where components such as motion capturing, human behaviour recognition, data decoding and extraction, and the application of the recognition result, can be deployed on separated end devices and edge nodes in an edge computing environment. Using a virtual reality application which can capture a user's motion and translate into the motion of a 3D avatar in real time, we successfully validate the effectiveness of the system and demonstrate its promising value to the research and application of human-centric software engineering. The demo video can be found at https://youtu.be/tnEshD8j-kA.