Rodrigo Spinola

Victoria Gomes, Delaney Selb, Fabio Palomba et al.

Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical workflows, yet their use remains fragmented, with no comprehensive synthesis to guide responsible adoption. Aims: This study analyzes how LLMs are used in ESE, examining supported tasks, phases of the empirical lifecycle, integration into workflows, reported benefits and limitations, and the extent of reproducibility-related reporting. It also identifies gaps and future research directions. Method: We conducted a systematic literature review of peer-reviewed papers (2020-2025) across 12 leading software engineering venues, resulting in 50 primary studies analyzed through qualitative and quantitative synthesis. Results: We identified 69 LLM-assisted tasks, mainly in mining software repositories and controlled experiments, focusing on classification, filtering, and evaluation. LLMs are used across multiple phases but are concentrated in data processing and analysis. Their integration is largely automation-oriented, with limited decision-support use. Benefits emphasize efficiency and scalability, while limitations include hallucinations, inconsistency, prompt sensitivity, and reproducibility issues. Reporting practices are often incomplete. Conclusion: LLM use in ESE is growing but remains automation-driven, with gaps in human-centered integration and transparency. We outline implications and research agenda for responsible use.

Robert Ramač, Vladimir Mandić, Nebojša Taušan et al.

The technical debt (TD) metaphor describes actions made during various stages of software development that lead to a more costly future regarding system maintenance and evolution. According to recent studies, on average 25% of development effort is spent, i.e. wasted, on TD caused issues in software development organizations. However, further research is needed to investigate the relations between various software development activities and TD. The objective of this study is twofold. First, to get empirical insight on the understanding and the use of the TD concept in the IT industry. Second, to contribute towards precise conceptualization of the TD concept through analysis of causes and effects. In order to address the research objective a family of surveys was designed as a part of an international initiative that congregates researchers from 12 countries -- InsighTD. At country level, national teams ran survey replications with industry practitioners from the respective countries. In total 653 valid responses were collected from 6 countries. Regarding the prevalence of the TD concept 22% of practitioners have only theoretical knowledge about it, and 47% have some practical experiences with TD identification or management. Further analysis indicated that senior practitioners who work in larger organizations, larger teams, and on larger systems are more likely to be experienced with TD management. Time pressure or deadline was the single most cited cause of TD. Regarding the effects of TD: delivery delay, low maintainability, and rework were the most cited. InsighTD is the first family of surveys on technical debt in software engineering. It provided a methodological framework that allowed multiple replication teams to conduct research activities and to contribute to a single dataset. Future work will focus on more specific aspects of TD management.

Daniel Méndez Fernández, Stefan Wagner, Marcos Kalinowski et al.

As part of the Naming the Pain in Requirements Engineering (NaPiRE) initiative, researchers compared problems that companies in Brazil and Germany encountered during requirements engineering (RE). The key takeaway was that in RE, human interaction is necessary for eliciting and specifying high-quality requirements, regardless of country, project type, or company size.