arXiv subjects

Jessica Diaz

Publications and source records attributed to Jessica Diaz.

5 recordsLinked to original sources

arXiv2026

Spec-Driven Development for Agentic Software Engineering: Harnessing Human-Agent Teamwork

Context: Software engineering is moving from AI-assisted practices like vibe coding, in which assistants accelerate individual developers, towards Agentic Software Engineering (ASE), in which autonomous agents are delegated goal-level tasks. However, industry reports a productivity paradox: as individual productivity increases, team throughput, review capacity, and stability degrade because team-scale software engineering discipline is neglected. Objective: This paper aims to establish the conceptual and methodological foundations of Spec-Driven Development (SDD) as an enabling discipline for ASE at team scale and characterize the harness, i.e., the technical and methodological mechanisms through which teams govern agent behavior. Method: We conducted a conceptual analysis drawing predominantly on gray literature, including ASE vision and roadmap papers, practitioner reports, talks, and tooling, because peer-reviewed evidence and a shared academic-industrial vocabulary are not yet established. Results: Using a comparative characterization of the paradigm progression as conceptual framing, the article presents (i) a socio-technical model of SDD in which specifications act as the contract substrate between humans and agents; (ii) an operational characterization of the harness, distinguishing the technical harness around the agent from the methodological harness around the team, with worked examples; and (iii) a typology of five human--agent interaction patterns through which the human role is redefined. Conclusion: We conclude that SDD reconstitutes, in specification-centric form, the contracts that vibe coding dissolves: accountability, verifiability, and transferability. Given the immaturity of the evidence base, this work is presented as a first step toward academic-industrial consensus rather than a validated theory, and outlines a research agenda for future empirical validation.

cs.SE↗

arXiv2026

Operationalizing Software Engineering Theories for Practical Validation

Software Engineering often adapts theory-building frameworks from the social sciences to address socio-technical complexity. The key phases of the theory-building process are conceptual development, operationalization, testing, and application. Operationalization translates abstract concepts into measurable elements for empirical validation. This phase is essential for delivering the practical utility required by an applied science like Software Engineering. We propose a systematic procedure for the operationalization phase that bridges the gap between abstract concepts and empirical validation, ensuring the resulting theory is both rigorous and practically useful. We extend the operationalization framework proposed by Sj{\o}berg et al. and formulate non-causal hypotheses following Dubin's approach. Our procedure defines variables, selects indicators, and systematically derives hypotheses. We present a replicable, evidence-based methodological guideline that preserves a clear chain of evidence and supports practical validation. We illustrate the procedure using the DevOps Team Taxonomies Theory. This guideline provides a transparent chain of evidence from theory to testable elements, empowering researchers to ground theoretical advancements in empirical evidence and deliver actionable insights for practitioners.

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arXiv2021

Why are many businesses instilling a DevOps culture into their organization?

Context: DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. Although this movement is relatively recent, there exist an intensive research around DevOps. However, the real reasons why companies move to DevOps and the results they expect to obtain have been paid little attention in real contexts. Objective: This paper aims to help practitioners and researchers to better understand the context and the problems that many companies face day to day in their organizations when they try to accelerate software delivery and the main drivers that move these companies to adopting DevOps. Method: We conducted an exploratory study by leveraging in depth, semi-structured interviews to relevant stakeholders of 30 multinational software-intensive companies, together industrial workshops and observations at organizations' facilities that supported triangulation. Additionally, we conducted an inter-coder agreement analysis, which is not usually addressed in qualitative studies in software engineering, to increase reliability and reduce authors bias of the drawn findings. Results: The research explores the problems and expected outcomes that moved companies to adopt DevOps and reveals a set of patterns and anti-patterns about the reasons why companies are instilling a DevOps culture. Conclusions: This study aims to strengthen evidence and support practitioners in making better informed about which problems trigger a DevOps transition and most common expected results.

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arXiv2021

Empirical Standards for Software Engineering Research

Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.

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arXiv2020

Reliability in Software Engineering Qualitative Research through Inter-Coder Agreement: A guide using Krippendorff's $\alpha$ & Atlas.ti

In recent years, the research on empirical software engineering that uses qualitative data analysis (e.g., cases studies, interview surveys, and grounded theory studies) is increasing. However, most of this research does not deep into the reliability and validity of findings, specifically in the reliability of coding in which these methodologies rely on, despite there exist a variety of statistical techniques known as Inter-Coder Agreement (ICA) for analyzing consensus in team coding. This paper aims to establish a novel theoretical framework that enables a methodological approach for conducting this validity analysis. This framework is based on a set of coefficients for measuring the degree of agreement that different coders achieve when judging a common matter. We analyze different reliability coefficients and provide detailed examples of calculation, with special attention to Krippendorff's $\alpha$ coefficients. We systematically review several variants of Krippendorff's $\alpha$ reported in the literature and provide a novel common mathematical framework in which all of them are unified through a universal $\alpha$ coefficient. Finally, this paper provides a detailed guide of the use of this theoretical framework in a large case study on DevOps culture. We explain how $\alpha$ coefficients are computed and interpreted using a widely used software tool for qualitative analysis like Atlas.ti. We expect that this work will help empirical researchers, particularly in software engineering, to improve the quality and trustworthiness of their studies.

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