arXiv subjects

Cristina Martinez Montes

Publications and source records attributed to Cristina Martinez Montes.

10 recordsLinked to original sources

arXiv2026

Exploring Emotional Intelligence in Software Testing

Background: Emotional Intelligence (EI) is the ability to recognise, understand, and manage one's own and others' emotions. Software testers deliver judgements about colleagues' work under deadlines they do not control, and prior work on emotion in software engineering has mostly studied developers. Aims: To explore how software testers describe the part EI plays in their day-to-day work, in communication and conflict within the team, and in responding to requirements volatility. Method: Semi-structured interviews with 16 software testers in Sweden working in teams that use agile practices, across aviation, automotive, healthcare, IT services, administration, banking and pharmaceuticals, analysed with reflexive thematic analysis informed by Goleman's EI framework. Results: Three themes. Testers described regulating stress under deadline pressure and drawing motivation from recognition, clarity and autonomy; managing the daily delivery of critical findings to colleagues so that trust survives; and responding to requirements change with frustration that turned into decisions about what to leave untested, into advocacy for process change, or into workarounds. Read against developer-focused studies, the themes point to features of the testing role: the work product is a criticism of a colleague's work, success is invisible while failure is attributed, and the tester's window shrinks with every upstream delay. Conclusions: For testers, managing emotions is a constant job requirement. The results highlight that the importance of EI increases when the development process lacks an independent testing phase. The findings also inform implications for teams and, ultimately, for organisations and future research.

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arXiv2026

Guidelines for Empirical Studies in Software Engineering involving Large Language Models

Large Language Models (LLMs) are widely used in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes how LLMs are used in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommendations (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the system and prompt design beyond the model; (4) report session traces, i.e., interaction logs and runtime traces; (5) use suitable baselines, benchmarks, and metrics; (6) include an open LLM as a baseline; (7) validate LLM outputs against human judgment; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines$.$org).

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arXiv2025

Large Language Models in Thematic Analysis: Prompt Engineering, Evaluation, and Guidelines for Qualitative Software Engineering Research

As artificial intelligence advances, large language models (LLMs) are entering qualitative research workflows, yet no reproducible methods exist for integrating them into established approaches like thematic analysis (TA), one of the most common qualitative methods in software engineering research. Moreover, existing studies lack systematic evaluation of LLM-generated qualitative outputs against established quality criteria. We designed and iteratively refined prompts for Phases 2-5 of Braun and Clarke's reflexive TA, then tested outputs from multiple LLMs against codes and themes produced by experienced researchers. Using 15 interviews on software engineers' well-being, we conducted blind evaluations with four expert evaluators who applied rubrics derived directly from Braun and Clarke's quality criteria. Evaluators preferred LLM-generated codes 61% of the time, finding them analytically useful for answering the research question. However, evaluators also identified limitations: LLMs fragmented data unnecessarily, missed latent interpretations, and sometimes produced themes with unclear boundaries. Our contributions are threefold. First, a reproducible approach integrating refined, documented prompts with an evaluation framework to operationalize Braun and Clarke's reflexive TA. Second, an empirical comparison of LLM- and human-generated codes and themes in software engineering data. Third, guidelines for integrating LLMs into qualitative analysis while preserving methodological rigour, clarifying when and how LLMs can assist effectively and when human interpretation remains essential.

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arXiv2025

A Multimodal Approach Combining Biometrics and Self-Report Instruments for Monitoring Stress in Programming: Methodological Insights

The study of well-being, stress and other human factors has traditionally relied on self-report instruments to assess key variables. However, concerns about potential biases in these instruments, even when thoroughly validated and standardised, have driven growing interest in alternatives in combining these measures with more objective methods, such as physiological measures. We aimed to (i) compare psychometric stress measures and biometric indicators and (ii) identify stress-related patterns in biometric data during software engineering tasks. We conducted an experiment where participants completed a pre-survey, then programmed two tasks wearing biometric sensors, answered brief post-surveys for each, and finally went through a short exit interview. Our results showed diverse outcomes; we found no stress in the psychometric instruments. Participants in the interviews reported a mix of feeling no stress and experiencing time pressure. Finally, the biometrics showed a significant difference only in EDA phasic peaks. We conclude that our chosen way of inducing stress by imposing a stricter time limit was insufficient. We offer methodological insights for future studies working with stress, biometrics, and psychometric instruments.

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arXiv2025

Evaluating the Impact of a Yoga-Based Intervention on Software Engineers' Well-Being

Software engineering tasks are high-stress and cognitively demanding. Additionally, there is a latent risk of software engineers presenting burnout, depression and anxiety. Established interventions in other fields centred around attention awareness have shown positive results in mental well-being. We aim to test how effective a yoga intervention is in improving general well-being in the workplace. For that, we designed, implemented and evaluated an eight-week yoga programme in a software development company. We used a mixed-methods data collection, using a survey of six psychometric scales, pre and post-intervention, and a weekly well-being scale during the programme. For method triangulation, we conducted a focus group with the organisers to obtain qualitative data. The quantitative results did not show any statistically significant improvement after the intervention. Meanwhile, the qualitative results illustrated that participants felt better and liked the intervention. We conclude that yoga has a positive impact, which, however, can easily get overlaid by contextual factors, especially with only a once-per-week intervention.

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arXiv2025

Emotional Strain and Frustration in LLM Interactions in Software Engineering

Large Language Models (LLMs) are increasingly integrated into various daily tasks in Software Engineering such as coding and requirement elicitation. Despite their various capabilities and constant use, some interactions can lead to unexpected challenges (e.g. hallucinations or verbose answers) and, in turn, cause emotions that develop into frustration. Frustration can negatively impact engineers' productivity and well-being if they escalate into stress and burnout. In this paper, we assess the impact of LLM interactions on software engineers' emotional responses, specifically strains, and identify common causes of frustration when interacting with LLMs at work. Based on 62 survey responses from software engineers in industry and academia across various companies and universities, we found that a majority of our respondents experience frustrations or other related emotions regardless of the nature of their work. Additionally, our results showed that frustration mainly stemmed from issues with correctness and less critical issues such as adaptability to context or specific format. While such issues may not cause frustration in general, artefacts that do not follow certain preferences, standards, or best practices can make the output unusable without extensive modification, causing frustration over time. In addition to the frustration triggers, our study offers guidelines to improve the software engineers' experience, aiming to minimise long-term consequences on mental health.

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arXiv2025

Factors Influencing Gender Representation in IT Faculty Programmes: Insights with a Focus on Software Engineering in a Nordic Context

Software engineering remains male-dominated despite efforts to attract and retain women. Many leave the field due to limited opportunities, unfair treatment, and challenging workplace cultures. Examining university life and choices is important, as these formative experiences shape career aspirations and can help address the root causes of underrepresentation in the industry. The study aimed to deepen understanding of the motivations behind women's choice of a career in IT, their experiences in academic life, and how these experiences influence their career decisions, all within a Nordic context. We used a combination of surveys in the bachelor programmes in the IT faculty and interviews with only women from software engineering (SE) to provide a comprehensive view of population experiences and a closer exploration of the experiences of a smaller sample with a focus on SE. Our results showed that family and personal interest are among the main factors motivating women to choose an IT programme. Further, women perceive more challenges following their chosen career path than men. We proposed high-level actions to address gender-related challenges and disparities based on our findings.

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arXiv2025

The Factors Influencing Well-Being in Software Engineers: A Cross-Country Mixed-Method Study

The well-being of software engineers is increasingly under strain due to the high-stress nature of their roles, which involve complex problem-solving, tight deadlines, and the pressures of rapidly evolving technologies. Despite increasing recognition of mental health challenges in software engineering, few studies focus on the factors that sustain or undermine well-being. Existing research often overlooks the interaction between personal, collaborative, and organisational influences on this unique population. This study fills this gap by investigating the specific factors affecting the well-being of software engineers. We conducted 15 qualitative interviews and complemented them with a confirmatory cross-country survey to validate and extend our findings to a broader population. Our mixed-methods approach provides a robust framework to identify key factors influencing well-being, including personal perceptions of well-being, interpersonal and collaborative dynamics, workplace support and recognition, organisational culture, and specific stressors inherent to software engineering. By offering a detailed, context-specific exploration of these factors, our study builds on existing literature and provides actionable insights for improving well-being in software engineering. We conclude with policy recommendations to inform organisational strategies and develop targeted interventions that address the specific challenges of this field, contributing to more sustainable and supportive work environments.

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arXiv2024

Qualifying and Quantifying the Benefits of Mindfulness Practices for IT Workers

The well-being and productivity of IT workers are crucial for both individual success and the overall prosperity of the organisations they serve. This study proposes mindfulness to alleviate stress and improve mental well-being for IT workers. During an 8-week program, IT workers learn about mindfulness, coupled with breathing practices. This study investigates the potential effects of these practices by analysing participants' reflections through thematic analysis and daily well-being ratings. The analysis showcased an increase in mental well-being and perceived productivity. It also indicated a change in the participants' perception, which showed increased self-awareness. The study recommends continuing the program in the industry to see its impact on work outputs.

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arXiv2022

Take a deep breath. Benefits of neuroplasticity practices for software developers and computer workers in a family of experiments

Context. Computer workers in general, and software developers specifically, are under a high amount of stress due to continuous deadlines and, often, over-commitment. Objective. This study investigates the effects of a neuroplasticity practice, a specific breathing practice, on the attention awareness, well-being, perceived productivity, and self-efficacy of computer workers. Method. We created a questionnaire mainly from existing, validated scales as entry and exit survey for data points for comparison before and after the intervention. The intervention was a 12-week program with a weekly live session that included a talk on a well-being topic and a facilitated group breathing session. During the intervention period, we solicited one daily journal note and one weekly well-being rating. We replicated the intervention in a similarly structured 8-week program. The data was analyzed using a Bayesian multi-level model for the quantitative part and thematic analysis for the qualitative part. Results. The intervention showed improvements in participants' experienced inner states despite an ongoing pandemic and intense outer circumstances for most. Over the course of the study, we found an improvement in the participants' ratings of how often they found themselves in good spirits as well as in a calm and relaxed state. We also aggregate a large number of deep inner reflections and growth processes that may not have surfaced for the participants without deliberate engagement in such a program. Conclusion. The data indicates usefulness and effectiveness of an intervention for computer workers in terms of increasing well-being and resilience. Everyone needs a way to deliberately relax, unplug, and recover. Breathing practice is a simple way to do so, and the results call for establishing a larger body of work to make this common practice.

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