arXiv subjects

Rashina Hoda

Publications and source records attributed to Rashina Hoda.

At least 37 records · Page 2Linked to original sources

arXiv2025

Manifestations of Empathy in Software Engineering: How, Why, and When It Matters

Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings provide insights into the expression of empathy in SE, the drivers behind empathetic practices, SE activities where empathy is perceived as useful or not, and the other factors that influence empathy. In addition, we offer practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

cs.SE↗

arXiv2025

An LLM-based multi-agent framework for agile effort estimation

Effort estimation is a crucial activity in agile software development, where teams collaboratively review, discuss, and estimate the effort required to complete user stories in a product backlog. Current practices in agile effort estimation heavily rely on subjective assessments, leading to inaccuracies and inconsistencies in the estimates. While recent machine learning-based methods show promising accuracy, they cannot explain or justify their estimates and lack the capability to interact with human team members. Our paper fills this significant gap by leveraging the powerful capabilities of Large Language Models (LLMs). We propose a novel LLM-based multi-agent framework for agile estimation that not only can produce estimates, but also can coordinate, communicate and discuss with human developers and other agents to reach a consensus. Evaluation results on a real-life dataset show that our approach outperforms state-of-the-art techniques across all evaluation metrics in the majority of the cases. Our human study with software development practitioners also demonstrates an overwhelmingly positive experience in collaborating with our agents in agile effort estimation.

cs.SE↗

arXiv2025

Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review

Following the rise of large language models (LLMs), many studies have emerged in recent years focusing on exploring the adoption of LLM-based tools for software development by novice developers: computer science/software engineering students and early-career industry developers with two years or less of professional experience. These studies have sought to understand the perspectives of novice developers on using these tools, a critical aspect of the successful adoption of LLMs in software engineering. To systematically collect and summarise these studies, we conducted a systematic literature review (SLR) following the guidelines by Kitchenham et al. on 80 primary studies published between April 2022 and June 2025 to answer four research questions (RQs). In answering RQ1, we categorised the study motivations and methodological approaches. In RQ2, we identified the software development tasks for which novice developers use LLMs. In RQ3, we categorised the advantages, challenges, and recommendations discussed in the studies. Finally, we discuss the study limitations and future research needs suggested in the primary studies in answering RQ4. Throughout the paper, we also indicate directions for future work and implications for software engineering researchers, educators, and developers. Our research artifacts are publicly available at https://github.com/Samuellucas97/SupplementaryInfoPackage-SLR.

cs.SE↗

arXiv2025

The Role of Humour in Software Engineering -- A Literature Review and Preliminary Taxonomy

Humour has long been recognized as a key factor in enhancing creativity, group effectiveness, and employee well-being across various domains. However, its occurrence and impact within software engineering (SE) teams remains under-explored. This paper introduces a comprehensive, literature review-based taxonomy exploring the characterisation and use of humour in SE teams, with the goal of boosting productivity, improving communication, and fostering a positive work environment while emphasising the responsible use of humour to mitigate its potential negative impacts. Drawing from a wide array of studies in psychology, sociology, and organizational behaviour, our proposed framework categorizes humour into distinct theories, styles, models, and scales, offering SE professionals and researchers a structured approach to understanding humour in their work. This study also addresses the unique challenges of applying humour in SE, highlighting its potential benefits while acknowledging the need for further empirical validation in this context. Ultimately, our study aims to pave the way for more cohesive, creative, and psychologically supportive SE environments through the strategic use of humour.

cs.SE↗

arXiv2025

Managing Technical Debt in a Multidisciplinary Data Intensive Software Team: an Observational Case Study

Context: There is an increase in the investment and development of data-intensive (DI) solutions, systems that manage large amounts of data. Without careful management, this growing investment will also grow associated technical debt (TD). Delivery of DI solutions requires a multidisciplinary skill set, but there is limited knowledge about how multidisciplinary teams develop DI systems and manage TD. Objective: This research contributes empirical, practice based insights about multidisciplinary DI team TD management practices. Method: This research was conducted as an exploratory observation case study. We used socio-technical grounded theory (STGT) for data analysis to develop concepts and categories that articulate TD and TDs debt management practices. Results: We identify TD that the DI team deals with, in particular technical data components debt and pipeline debt. We explain how the team manages the TD, assesses TD, what TD treatments they consider and how they implement TD treatments to fit sprint capacity constraints. Conclusion: We align our findings to existing TD and TDM taxonomies, discuss their implications and highlight the need for new implementation patterns and tool support for multidisciplinary DI teams.

cs.SE↗

arXiv2025

Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: A Pilot Study

This study evaluates how well large language models (LLMs) and traditional machine translation (MT) tools translate medical consultation summaries from English into Arabic, Chinese, and Vietnamese. It assesses both patient, friendly and clinician, focused texts using standard automated metrics. Results showed that traditional MT tools generally performed better, especially for complex texts, while LLMs showed promise, particularly in Vietnamese and Chinese, when translating simpler summaries. Arabic translations improved with complexity due to the language's morphology. Overall, while LLMs offer contextual flexibility, they remain inconsistent, and current evaluation metrics fail to capture clinical relevance. The study highlights the need for domain-specific training, improved evaluation methods, and human oversight in medical translation.

cs.CL↗

arXiv2025

Guiding Principles for Using Mixed Methods Research in Software Engineering

Mixed methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs. This paper provides guiding principles and advice on how to design mixed method research, and to encourage the intentional, rigorous, and innovative application of mixed methods in software engineering. It also presents key properties of core mixed method research designs. Through a number of fictitious but recognizable software engineering research scenarios, we showcase how to choose suitable designs and consider the inevitable trade-offs any design choice leads to. We describe several antipatterns that illustrate what to avoid in mixed method research, and when mixed method research should be considered over other approaches.

cs.SE↗

arXiv2025

Qualitative Research Methods in Software Engineering: Past, Present, and Future

The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influential papers from the third decade of TSE's 50 years history. In this retrospective, the authors discuss the evolution of the use of qualitative methods in software engineering research, the impact it's had on research and practice, and reflections on what is coming and deserves attention.

cs.SE↗

arXiv2025

What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

cs.SE↗

arXiv2024

Raising AI Ethics Awareness through an AI Ethics Quiz for Software Practitioners

Context:Today, ethical issues surrounding AI systems are increasingly prevalent, highlighting the critical need to integrate AI ethics into system design to prevent societal harm. Raising awareness and fostering a deep understanding of AI ethics among software practitioners is essential for achieving this goal. However, research indicates a significant gap in practitioners' awareness and knowledge of AI ethics and ethical principles. While much effort has been directed toward helping practitioners operationalise AI ethical principles such as fairness, transparency, accountability, and privacy, less attention has been paid to raising initial awareness, which should be the foundational step. Objective: Addressing this gap, we developed a software-based tool, the AI Ethics Quiz, to raise awareness and enhance the knowledge of AI ethics among software practitioners. Our objective was to organise interactive workshops, introduce the AI Ethics Quiz, and evaluate its effectiveness in enhancing awareness and knowledge of AI ethics and ethical principles among practitioners. Method: We conducted two one-hour workshops (one in-person and one online) involving 29 software practitioners. Data was collected through pre-quiz questionnaire, the AI Ethics Quiz, and a post-quiz questionnaire. Results: The anonymous responses revealed that the quiz significantly improved practitioners' awareness and understanding of AI ethics. Additionally, practitioners found the quiz engaging and reported it created a meaningful learning experience regarding AI ethics. In this paper, we share insights gained from conducting these interactive workshops and introducing the AI Ethics Quiz to practitioners. Conclusion: We also provide recommendations for software companies and leaders to adopt similar initiatives, which may help them enhance practitioners' awareness and understanding of AI ethics.

cs.SE↗

arXiv2024

Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development

The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the perspectives and experiences of AI practitioners in developing a fair AI/ML system. Understanding AI practitioners' perspectives and experiences on the fairness of AI/ML systems are important because they are directly involved in its development and deployment and their insights can offer valuable real-world perspectives on the challenges associated with ensuring fairness in AI/ML systems. We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML system, the consequences of developing an unfair AI/ML system, and the strategies they employ to ensure AI/ML system fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' system and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML system, and (iii) strategies used to ensure AI/ML system fairness. By exploring AI practitioners' perspectives and experiences, this study provides actionable insights to enhance AI/ML fairness, which may promote fairer systems, reduce bias, and foster public trust in AI technologies. Additionally, we also identify areas for further investigation and offer recommendations to aid AI practitioners and AI companies in navigating fairness.

cs.CY↗

arXiv2024

Understanding the Influence of Motivation on Requirements Engineering-related Activities

Context: Requirements Engineering (RE)-related activities are critical in developing quality software and one of the most human-dependent processes in software engineering (SE). Hence, identifying the impact of diverse human-related aspects on RE is crucial in the SE context. Objective: Our study explores the impact of one of the most influential human aspects, motivation on RE, aiming to deepen understanding and provide practical guidance. Method: By conducting semi-structured interviews with 21 RE-involved practitioners, we developed a theory using socio-technical grounded theory(STGT) that explains the contextual, causal, and intervening conditions influencing motivation in RE-related activities. Result: We identified strategies to enhance motivating situations or mitigate demotivating ones, and the consequences resulting from applying these strategies. Conclusion: Our findings offer actionable insights for software practitioners to manage the influence of motivation on RE and help researchers further investigate its role across various SE contexts in the future.

cs.SE↗

arXiv2024

What do AI/ML practitioners think about AI/ML bias?

AI leaders and companies have much to offer to AI/ML practitioners to support them in addressing and mitigating biases in the AI/ML systems they develop. AI/ML practitioners need to receive the necessary resources and support from experts to develop unbiased AI/ML systems. However, our studies have revealed a discrepancy between practitioners' understanding of 'AI/ML bias' and the definitions of tech companies and researchers. This indicates a misalignment that needs addressing. Efforts should be made to match practitioners' understanding of AI/ML bias with the definitions developed by tech companies and researchers. These efforts could yield a significant return on investment by aiding AI/ML practitioners in developing unbiased AI/ML systems.

cs.SE↗

arXiv2024

The Impact of Human Aspects on the Interactions Between Software Developers and End-Users in Software Engineering: A Systematic Literature Review

Context: Research on human aspects within the field of software engineering (SE) has been steadily gaining prominence in recent years. These human aspects have a significant impact on SE due to the inherently interactive and collaborative nature of the discipline. Objective: In this paper, we present a systematic literature review (SLR) on human aspects affecting developer-user interactions. The objective of this SLR is to plot the current landscape of primary studies by examining the human aspects that influence developer-user interactions, their implications, interrelationships, and how existing studies address these implications. Method: We conducted this SLR following the guidelines proposed by Kitchenham et al. We performed a comprehensive search in six digital databases, and an exhaustive backward and forward snowballing process. We selected 46 primary studies for data extraction. Results: We identified various human aspects affecting developer-user interactions in SE, assessed their interrelationships, identified their positive impacts and mitigation strategies for negative effects. We present specific recommendations derived from the identified research gaps. Conclusion: Our findings suggest the importance of leveraging positive effects and addressing negative effects in developer-user interactions through the implementation of effective mitigation strategies. These insights may benefit software practitioners for effective user interactions, and the recommendations proposed by this SLR may aid the research community in further human aspects related studies.

cs.SE↗

arXiv2024

Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review

The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems -- the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners' views on ethics in AI and analysed them to derive five categories: practitioner awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from practitioners' viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI practitioners when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for practitioners, managers, and organisations to help in their efforts to better consider and implement ethics in AI.

cs.SE↗

arXiv2024

Enablers and Barriers of Empathy in Software Developer and User Interaction: A Mixed Methods Case Study

Software engineering (SE) requires developers to collaborate with stakeholders, and understanding their emotions and perspectives is often vital. Empathy is a concept characterising a person's ability to understand and share the feelings of another. However, empathy continues to be an under-researched human aspect in SE. We studied how empathy is practised between developers and end users using a mixed methods case study. We used an empathy test, observations and interviews to collect data, and socio technical grounded theory and descriptive statistics to analyse data. We identified the nature of awareness required to trigger empathy and enablers of empathy. We discovered barriers to empathy and a set of potential strategies to overcome these barriers. We report insights on emerging relationships and present a set of recommendations and potential future works on empathy and SE for software practitioners and SE researchers.

cs.SE↗

arXiv2024

Challenges, Adaptations, and Fringe Benefits of Conducting Software Engineering Research with Human Participants during the COVID-19 Pandemic

The COVID-19 pandemic changed the way we live, work and the way we conduct research. With the restrictions of lockdowns and social distancing, various impacts were experienced by many software engineering researchers, especially whose studies depend on human participants. We conducted a mixed methods study to understand the extent of this impact. Through a detailed survey with 89 software engineering researchers working with human participants around the world and a further nine follow-up interviews, we identified the key challenges faced, the adaptations made, and the surprising fringe benefits of conducting research involving human participants during the pandemic. Our findings also revealed that in retrospect, many researchers did not wish to revert to the old ways of conducting human-oriented research. Based on our analysis and insights, we share recommendations on how to conduct remote studies with human participants effectively in an increasingly hybrid world when face-to-face engagement is not possible or where remote participation is preferred.

cs.SE↗

arXiv2023

Teaching Software Ethics to Future Software Engineers

The importance of teaching software ethics to software engineering (SE) students is more critical now than ever before as software related ethical issues continue to impact society at an alarming rate. Traditional classroom methods, vignettes, role-play games, and quizzes have been employed over the years to teach SE students about software ethics. Recognising the significance of incorporating software ethics knowledge in SE education and the continued need for more efforts in the area of the teaching and learning of SE ethics, we developed an interactive, scenario-based Software Ethics Quiz. Our goal was to teach SE students about ethics in a comprehensive, open, and engaging manner through a combined approach of an online lecture followed by an interactive workshop with the quiz and a debriefing session. The anonymous quiz responses collected showed promising results regarding the engagement and efficacy of the lecture and quiz, with a slightly better rating for the interactive quiz. The voluntary student feedback collected suggested that a majority of the participants found the debrief discussion on the quiz scenarios to be very beneficial for learning about software ethics. In this experience report, we share our experiences, related educational resources including the quiz, and recommendations from lessons learned with the wider education community to keep driving this critical topic forward

cs.SE↗