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Documents authored by de Souza Santos, Ronnie

Document
Technical Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.6
The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys

Authors: Ronnie de Souza Santos, Italo Santos, Maria Teresa Baldassarre, Cleyton Magalhães, and Mairieli Wessel

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
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.
Cite as

Ronnie de Souza Santos, Italo Santos, Maria Teresa Baldassarre, Cleyton Magalhães, and Mairieli Wessel. The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 6:1-6:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{desouzasantos_et_al:LIPIcs.ESEM.2026.6,
  author =	{de Souza Santos, Ronnie and Santos, Italo and Baldassarre, Maria Teresa and Magalh\~{a}es, Cleyton and Wessel, Mairieli},
  title =	{{The Influence of Fraudulent AI-Generated Responses on Software Engineering Surveys}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{6:1--6:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.6},
  URN =		{urn:nbn:de:0030-drops-279748},
  doi =		{10.4230/LIPIcs.ESEM.2026.6},
  annote =	{Keywords: LLMs, survey, threats to validity}
}
Document
Emerging Results, Vision & Reflection Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.61
How Many Interviews Are Enough in a Software Engineering Study? Preliminary Findings on Sample Size and Saturation

Authors: Ronnie de Souza Santos, Italo Santos, Mauricio Rodrigues Lima, and Cleyton Magalhães

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
Background. Interview-based studies are widely used in empirical software engineering to investigate human, organizational, and socio-technical phenomena, yet interview sample adequacy and saturation are reported inconsistently across the literature. Aims. This paper investigates how interview sample adequacy and saturation are reported in empirical software engineering research. Method. We analyzed 427 papers published between 2016 and 2025 across major software engineering venues, focusing on interview sample sizes, saturation discussions, and sample adequacy justifications. Results. Preliminary findings indicate substantial variation in interview sample sizes, ranging from highly specialized small-sample studies to broader investigations involving large interview datasets. Studies involving 12 or fewer interviewees were common and frequently associated with specialized industrial contexts or constrained organizational access. However, the most common range was 13 to 24 interviewees, suggesting that moderate-sized samples represent the most common configuration in empirical software engineering research. Saturation and sample adequacy justifications were heterogeneous, with many studies relying on implicit or contextual reasoning rather than explicit methodological discussion. Conclusions. Our findings provide empirical insights into methodological reporting practices in interview-based software engineering research and contribute to ongoing discussions on qualitative rigor and transparency.
Cite as

Ronnie de Souza Santos, Italo Santos, Mauricio Rodrigues Lima, and Cleyton Magalhães. How Many Interviews Are Enough in a Software Engineering Study? Preliminary Findings on Sample Size and Saturation. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 61:1-61:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{desouzasantos_et_al:LIPIcs.ESEM.2026.61,
  author =	{de Souza Santos, Ronnie and Santos, Italo and Lima, Mauricio Rodrigues and Magalh\~{a}es, Cleyton},
  title =	{{How Many Interviews Are Enough in a Software Engineering Study? Preliminary Findings on Sample Size and Saturation}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{61:1--61:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.61},
  URN =		{urn:nbn:de:0030-drops-280299},
  doi =		{10.4230/LIPIcs.ESEM.2026.61},
  annote =	{Keywords: qualitative research, interviews, publications}
}
Document
Emerging Results, Vision & Reflection Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.69
Beyond Individual Prompting Efficiency: A Socio-Technical Perspective on Computational Efficiency in AI-Assisted Software Engineering

Authors: Darja Smite, Viggo Tellefsen Wivestad, Gabrielle O'Brien, Italo Santos, Giuseppe Destefanis, Maria Teresa Baldassarre, Mircea Lungu, Elda Paja, and Ronnie de Souza Santos

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
As Artificial Intelligence (AI) assistants and agents become increasingly integrated into software engineering work, concerns about the economic and environmental costs of AI usage continue to grow. Current discussions of computational efficiency focus primarily on model architectures, hardware optimization, and inference cost reduction. In this vision paper, we argue that computational efficiency should also be understood as a socio-technical phenomenon shaped by patterns of human-AI interaction within organizational settings. Motivated by ongoing dialogues with industry partners and informed by the Theory of Planned Behavior and Social Cognitive Theory, we introduce the concept of computational behavior to describe patterns of AI-assisted work that influence how computational resources are consumed, reused, coordinated, and amplified across software engineering activities. Building on this perspective, we propose a multilevel conceptual framework linking individual computational behaviors to emergent collective computational outcomes. We argue that individually rational AI usage behaviors may accumulate into either computational waste or collective computational efficiency depending on how interactions are shared, reused, and aligned across teams and workflows. While fragmented AI use may generate duplicated prompting, redundant generation, and coordination overhead, coordinated collective practices, such as reusable prompts, shared contextualization, and workflow integration may improve collective computational efficiency. We conclude by outlining a research agenda for studying computational coordination, including empirical and organizational AI governance in AI-assisted software engineering.
Cite as

Darja Smite, Viggo Tellefsen Wivestad, Gabrielle O'Brien, Italo Santos, Giuseppe Destefanis, Maria Teresa Baldassarre, Mircea Lungu, Elda Paja, and Ronnie de Souza Santos. Beyond Individual Prompting Efficiency: A Socio-Technical Perspective on Computational Efficiency in AI-Assisted Software Engineering. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 69:1-69:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{smite_et_al:LIPIcs.ESEM.2026.69,
  author =	{Smite, Darja and Wivestad, Viggo Tellefsen and O'Brien, Gabrielle and Santos, Italo and Destefanis, Giuseppe and Baldassarre, Maria Teresa and Lungu, Mircea and Paja, Elda and de Souza Santos, Ronnie},
  title =	{{Beyond Individual Prompting Efficiency: A Socio-Technical Perspective on Computational Efficiency in AI-Assisted Software Engineering}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{69:1--69:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.69},
  URN =		{urn:nbn:de:0030-drops-280377},
  doi =		{10.4230/LIPIcs.ESEM.2026.69},
  annote =	{Keywords: AI-assisted software engineering, Computational waste, Team practices, Mental models, Empirical software engineering, Vision, Research agenda}
}

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