arXiv subjects

Maria Teresa Baldassarre

Publications and source records attributed to Maria Teresa Baldassarre.

At least 19 recordsLinked to original sources

arXiv2026

The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys

Background: Large Language Models (LLMs) introduce new concerns regarding fraudulent or AI assisted participation in software engineering surveys. Aims: This study investigates how suspicious or potentially AI assisted responses may affect the validity of software engineering survey findings. Method: We conducted a secondary analysis of four software engineering survey datasets using manual identification of suspicious responses, automated AI generated text detection, descriptive statistical analysis, and thematic analysis. We compared findings obtained from the original and manually cleaned datasets. Results: Quantitative findings generally remained stable after filtering suspicious responses, although some demographic and analytical variables showed moderate variation, affecting the interpretation of specific participant groups and contextual characteristics. In contrast, qualitative findings were more strongly influenced by changes in contextual framing, code prominence, and the nature of the evidence supporting interpretation, shaping how participants' experiences and study contexts were interpreted and characterized. Conclusions: AI assisted participation may influence software engineering survey findings differently depending on the type of analysis being conducted. The findings reinforce the importance of combining multiple validation procedures, particularly in studies relying on open ended responses.

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arXiv2026

How Software Engineering Students Use LLMs to Write Research Papers: An Experience Report

Large language models are increasingly becoming part of software engineering education, including activities involving empirical software engineering and evidence synthesis. This paper reports an educational experience involving the integration of reflective LLM use into an empirical methods assignment in a third-year software architecture course. Students were asked to develop a short research paper using either a rapid review or a gray literature review methodology and to disclose how LLMs were used throughout the assignment. We analyzed 146 student disclosure statements using a cross-analysis process combining LLM-assisted categorization with manual verification and refinement by the researchers. The reflections describe how students incorporated LLMs during activities such as brainstorming, methodological clarification, organization of findings, and writing refinement, while also reporting concerns regarding inaccuracies and verification of generated content. This experience report discusses lessons learned and educational implications for integrating AI-assisted technologies into empirical software engineering education.

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arXiv2026

A Quasi-Experimental Evaluation of Coaching to Mitigate the Impostor Phenomenon in Early-Career Software Engineers

Context: The Impostor Phenomenon (IP), the persistent belief of being a fraud despite evident competence, is common in Software Engineering (SE), where high expectations for expertise and innovation prevail. Although coaching and similar interventions are proposed to mitigate IP, empirical evidence in SE remains underexplored. Objective: This study examines the impact of a structured group coaching intervention on reducing IP feelings among early-career software engineers. Method: We conducted a quasi-experiment with 20 participants distributed across two project teams using a wait-list control design, complemented by non-participant observation. The treatment group received a three-session coaching intervention, while the control group received it after an observation phase. IP was assessed using the Clance Impostor Phenomenon Scale (CIPS), alongside evaluated measures of well-being (WHO-5), life satisfaction (SWLS), and affect (PANAS). Results: The coaching resulted in modest reductions in CIPS scores, whereas the control group also improved during the observation phase, suggesting that contextual and temporal factors may have exerted a stronger influence than the formal intervention. Conclusion: These results suggest that coaching may support reflection and awareness related to IP, yet other contextual aspects of team collaboration and project work might also contribute to these changes. This study offers a novel empirical step toward understanding how structured IP interventions operate within SE environments.

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arXiv2026

The role of team diversity in AI systems development

The widespread integration of AI technologies has intensified concerns about fairness and bias, as these systems often perpetuate societal inequalities through flawed data and design choices. While software engineering research has largely concentrated on technical solutions, such as improving datasets and models, the social dynamics that shape AI outcomes remain underexplored. This study investigates the role of team diversity in the development of AI systems. Drawing from the experience of four AI focused teams working in a large software company operating in Brazil and Portugal, and collaborating with global clients, the study explores how diverse teams influence the development of AI systems. Using Grounded Theory, we conducted 25 interviews with software professionals involved in projects spanning domains such as education, energy, accessibility, and facial recognition. Although our study is conducted in an organizational setting, the variety of projects, from regional to multinational, ensures exposure to global development practices and diverse team dynamics, bringing a variety of perspectives into our findings. Our analysis revealed six key roles that team diversity played in AI development: diversifying perspectives for bias identification, bringing empathy to AI development, addressing systemic discrimination, supporting inclusive and participatory decision making, using diversity as a safeguard against bias, and fostering broadened thinking in problem solving. These findings highlight the importance of incorporating diverse perspectives in AI projects and offer practical recommendations for integrating fairness considerations into software development practices.

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arXiv2026

Validation of an analyzability model for quantum software: a family of experiments

The analyzability of hybrid software, which integrates both classical and quantum components, is a key factor in ensuring its maintainability and industrial adoption. This article presents the empirical validation, through a family of experiments, of the quantum component of a previously proposed hybrid software analyzability model based on the ISO/IEC 25010 standard. The experimental series consists of four studies involving participants with diverse profiles in both academic and professional settings. In these experiments, the model's ability to effectively measure the analyzability of quantum algorithms is assessed, and the relationship between the analyzability levels computed by the model and the participant's perceptions of the complexity of these algorithms is examined. The results indicate that the proposed model effectively distinguishes between quantum software components with varying levels of analyzability and aligns with human perception, reinforcing its validity in quantum computing.

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arXiv2026

Industry Expectations and Skill Demands in Quantum Software Testing

Quantum software testing introduces new challenges that differ fundamentally from those in classical software engineering. Aims: This study investigates how the quantum software industry defines testing roles and what skills are expected from professionals in these positions. Method: We analyzed 110 job postings from organizations involved in quantum software and hardware development, identifying activities, competencies, and skill requirements related to testing. Results: The findings show that testing in quantum contexts combines traditional software quality assurance with experimental validation, emphasizing calibration, control, and hybrid quantum-classical verification. Employers seek professionals who integrate programming and automation expertise with quantum-specific technical knowledge and interdisciplinary collaboration skills. Conclusions: Quantum software testing remains at an early but rapidly evolving stage that bridges software engineering and experimental physics, highlighting the need for educational and research efforts that align testing practices with industrial realities.

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arXiv2026

Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study

Context: Generative Artificial Intelligence (GenAI) tools, such as GitHub Copilot and GPT tools, represent a paradigm shift in software engineering. While their impact is clear, most studies are short-term, focused on individual experiments. The sustained, team-level effects on productivity within industrial agile environments remain largely uncharacterized. Goal: This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams. We characterize its effect on developers' productivity by applying the multi-dimensional SPACE framework. Method: We conducted a multi-case longitudinal study involving 3 agile teams at a large technology consulting firm for around 13 months. We collected and compared quantitative telemetry (Jira, SonarQube, Git) and qualitative survey data from historical (pre-adoption) and research (post-adoption) sprints. Conclusion: GenAI tools can significantly improve team performance and well-being. Our key finding is a sharp increase in Performance and perceived Efficiency concurrent with flat developer Activity. This suggests GenAI increases the value density of development work, not its volume. This finding validates the necessity of multi-dimensional frameworks like SPACE to capture the true, nuanced impact of GenAI in situ, which would be invisible to studies measuring Activity alone.

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arXiv2026

Impostor Phenomenon as Human Debt: A Challenge to the Future of Software Engineering

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

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arXiv2026

Constructive Patterns for Human-Centered Tech Hiring

[Context] Online Recruitment and Selection (R&S) processes are often the first point of contact between early-career software engineers and the tech industry. Yet many candidates experience these processes as opaque, inefficient, or even discouraging. While prior research has extensively documented the flaws and biases in online tech hiring, little is known about the practices that create positive candidate experiences. [Objective & Method] This paper explores such practices, referred to as Constructive Patterns (CPs), from the perspective of early-career software engineers. Guided by Applicant Attribution-Reaction Theory, we conducted 22 semi-structured interviews in which participants collectively described over 470 online R&S experiences. [Results] Through thematic analysis, we identified 22 CPs that reflect positive practices such as comprehensive and transparent job advertisements (CP01), specific and developmental feedback (CP03), humanized and respectful interaction (CP06), and framing the process as a two-way street (CP18). [Conclusion] Our findings extend the conversation on tech hiring beyond diagnosing dysfunctions toward designing for human-centered and growth-oriented candidate experiences. The resulting catalog of CPs provides a concrete and empirically grounded resource for organizations seeking to attract and support early-career software engineers more effectively.

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arXiv2026

Towards an OSF-based Registered Report Template for Software Engineering Controlled Experiments

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

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arXiv2025

An Investigation on How AI-Generated Responses Affect SoftwareEngineering Surveys

Survey research is a fundamental empirical method in software engineering, enabling the systematic collection of data on professional practices, perceptions, and experiences. However, recent advances in large language models (LLMs) have introduced new risks to survey integrity, as participants can use generative tools to fabricate or manipulate their responses. This study explores how LLMs are being misused in software engineering surveys and investigates the methodological implications of such behavior for data authenticity, validity, and research integrity. We collected data from two survey deployments conducted in 2025 through the Prolific platform and analyzed the content of participants' answers to identify irregular or falsified responses. A subset of responses suspected of being AI generated was examined through qualitative pattern inspection, narrative characterization, and automated detection using the Scribbr AI Detector. The analysis revealed recurring structural patterns in 49 survey responses indicating synthetic authorship, including repetitive sequencing, uniform phrasing, and superficial personalization. These false narratives mimicked coherent reasoning while concealing fabricated content, undermining construct, internal, and external validity. Our study identifies data authenticity as an emerging dimension of validity in software engineering surveys. We emphasize that reliable evidence now requires combining automated and interpretive verification procedures, transparent reporting, and community standards to detect and prevent AI generated responses, thereby protecting the credibility of surveys in software engineering.

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arXiv2025

Attributes to Support the Formulation of Practically Relevant Research Problems in Software Engineering

[Background] A well-formulated research problem is essential for achieving practical relevance in Software Engineering (SE), yet there is a lack of structured guidance in this early phase. [Aims] Our goal is to introduce and evaluate seven attributes identified in the SE literature as relevant for formulating research problems (practical problem, context, implications/impacts, practitioners, evidence, objective, and research questions) in terms of their perceived importance and completeness, and learn how they can be applied. [Method] We conducted a workshop with 42 senior SE researchers during the ISERN 2024 meeting. The seven attributes were presented using a Problem Vision board filled with a research example. Participants discussed attributes in groups, shared written feedback, and individually completed a survey assessing their importance, completeness, and suggestions for improvement. [Results] The findings confirm the importance of the seven attributes in the formulation of industry-oriented research problems. Qualitative feedback illustrated how they can be applied in practice and revealed suggestions to refine them, such as incorporating financial criteria (e.g., ROI) into implications/impacts and addressing feasibility and constraints under evidence. [Conclusion] The results reaffirm the importance of the seven attributes in supporting a reflective and context-aware problem formulation. Adapting their use to specific research contexts can help to improve the alignment between academic research and industry needs.

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arXiv2025

Towards AI Agents Supported Research Problem Formulation

Poorly formulated research problems can compromise the practical relevance of Software Engineering studies by not reflecting the complexities of industrial practice. This vision paper explores the use of artificial intelligence agents to support SE researchers during the early stage of a research project, the formulation of the research problem. Based on the Lean Research Inception framework and using a published study on code maintainability in machine learning as a reference, we developed a descriptive evaluation of a scenario illustrating how AI agents, integrated into LRI, can support SE researchers by pre filling problem attributes, aligning stakeholder perspectives, refining research questions, simulating multiperspective assessments, and supporting decision making. The descriptive evaluation of the scenario suggests that AI agent support can enrich collaborative discussions and enhance critical reflection on the value, feasibility, and applicability of the research problem. Although the vision of integrating AI agents into LRI was perceived as promising to support the context aware and practice oriented formulation of research problems, empirical validation is needed to confirm and refine the integration of AI agents into problem formulation.

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arXiv2025

Towards Lean Research Inception: Assessing Practical Relevance of Formulated Research Problems

[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry connections, and poorly defined research problems. Clear criteria for evaluating SE research problems can help align their value, feasibility, and applicability with industrial needs. [Goal] In this paper, we introduce the Lean Research Inception (LRI) framework, designed to support the formulation and assessment of practically relevant research problems in SE. We describe its initial evaluation strategy conducted in a workshop with a network of SE researchers experienced in industry-academia collaboration and report the evaluation of its three assessment criteria (valuable, feasible, and applicable) regarding their importance in assessing practical relevance. [Method] We applied LRI retroactively to a published research paper, engaging workshop participants in discussing and assessing the research problem by applying the proposed criteria using a semantic differential scale. Participants provided feedback on the criteria's importance and completeness, drawn from their own experiences in industry-academia collaboration. [Results] The findings reveal an overall agreement on the importance of the three criteria - valuable (83.3%), feasible (76.2%), and applicable (73.8%) - for aligning research problems with industrial needs. Qualitative feedback suggested adjustments in terminology with a clearer distinction between feasible and applicable, and refinements for valuable by more clearly considering business value, ROI, and originality. [Conclusion] While LRI constitutes ongoing research and requires further evaluation, our results strengthen our confidence that the three criteria applied using the semantic differential scale can already help the community assess the practical relevance of SE research problems.

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arXiv2025

Who "Controls" Where Work Shall be Done? State-of-Practice in Post-Pandemic Remote Work Regulation

The COVID-19 pandemic has permanently altered workplace structures, making remote work a widespread practice. While many employees advocate for flexibility, many employers reconsider their attitude toward remote work and opt for structured return-to-office mandates. Media headlines repeatedly emphasize that the corporate world is returning to full-time office work. This study examines how companies employing software engineers and supporting roles regulate work location, whether corporate policies have evolved in the last five years, and, if so, how, and why. We collected data on remote work regulation from corporate HR and/or management representatives from 68 corporate entities that vary in size, location, and orientation towards remote or office work. Our findings reveal that although many companies prioritize office-centred working (50%), most companies in our sample permit hybrid working to varying degrees (85%). Remote work regulation does not reveal any particular new "best practice" as policies differ greatly, but the single most popular arrangement was the three in-office days per week. More than half of the companies (51%) encourage or mandate office days, and more than quarter (28%) have changed regulations, gradually increasing the mandatory office presence or implementing differentiated conditions. Although no companies have increased flexibility, only four companies are returning to full-time office work. Our key recommendation for office-oriented companies is to consider a trust-based alternative to strict office presence mandates, while for companies oriented toward remote working, we warn about the points of no (or hard) return. Finally, the current state of policies is clearly not final, as companies continue to experiment and adjust their work regulation.

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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.

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arXiv2025

Can Machine Learning Support the Selection of Studies for Systematic Literature Review Updates?

[Background] Systematic literature reviews (SLRs) are essential for synthesizing evidence in Software Engineering (SE), but keeping them up-to-date requires substantial effort. Study selection, one of the most labor-intensive steps, involves reviewing numerous studies and requires multiple reviewers to minimize bias and avoid loss of evidence. [Objective] This study aims to evaluate if Machine Learning (ML) text classification models can support reviewers in the study selection for SLR updates. [Method] We reproduce the study selection of an SLR update performed by three SE researchers. We trained two supervised ML models (Random Forest and Support Vector Machines) with different configurations using data from the original SLR. We calculated the study selection effectiveness of the ML models for the SLR update in terms of precision, recall, and F-measure. We also compared the performance of human-ML pairs with human-only pairs when selecting studies. [Results] The ML models achieved a modest F-score of 0.33, which is insufficient for reliable automation. However, we found that such models can reduce the study selection effort by 33.9% without loss of evidence (keeping a 100% recall). Our analysis also showed that the initial screening by pairs of human reviewers produces results that are much better aligned with the final SLR update result. [Conclusion] Based on our results, we conclude that although ML models can help reduce the effort involved in SLR updates, achieving rigorous and reliable outcomes still requires the expertise of experienced human reviewers for the initial screening phase.

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arXiv2025

Impostor Phenomenon Among Software Engineers: Investigating Gender Differences and Well-Being

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.

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