CONTEXT. Focus Group (FG) is a qualitative technique that collects data through moderated group discussion. Although widely used in fields where human behavior is central, their use in Software Engineering (SE) is comparatively less common. In SE, FG applications often need to accommodate technical domains, participants with specialized expertise, and research goals that differ from those typically addressed in traditional social science settings. OBJECTIVE. This study analyzes four FG experiences conducted to investigate distinct SE technologies and topics. The objective is to examine how design decisions, execution strategies, and contextual conditions support or hinder the identification of the benefits and drawbacks of using FGs in SE research. METHOD. We performed a reflective cross-case methodological analysis of four FGs conducted in both academic and industrial settings. The studies differed in goals, group configurations, participant expertise, and moderation strategies. Data sources included session transcripts, written artifacts, moderator observations, and participant feedback. RESULTS. The results indicate that data quality depended on participants’ prior knowledge, the moderator's impartiality, the execution context, and the use of multimodal data capture. Depth and generalizability were limited by time constraints, uneven engagement, and the diverse qualitative data that hindered comparison. CONCLUSIONS. FG is an effective complementary method in SE research, particularly for exploratory analysis and for understanding practitioner perspectives on SE technologies. However, its effectiveness depends on deliberate design choices, including participant selection and preparation, group configuration, and systematic documentation.

Background: As empirical software engineering evolves, more studies adopt data strategies-approaches that investigate digital artifacts such as models, source code, or system logs rather than relying on human subjects. Synthesizing results from such studies introduces new methodological challenges. Aims: This study assesses the effects of model quantization on correctness and resource efficiency in deep learning (DL) systems. Additionally, it explores the methodological implications of aggregating evidence from empirical studies that adopt data strategies. Method: We conducted a research synthesis of six primary studies that evaluate model quantization. We applied the Structured Synthesis Method (SSM) to aggregate the findings, which combines qualitative and quantitative evidence through diagrammatic modeling. A total of 19 evidence models were extracted and aggregated. Results: The aggregated evidence indicates that model quantization weakly negatively affects correctness metrics while consistently improving resource efficiency metrics, including storage size, inference latency, and GPU energy consumption-a manageable trade-off for many DL deployment contexts. Evidence across quantization techniques remains fragmented, underscoring the need for more focused empirical studies per technique. Conclusions: Model quantization offers substantial efficiency benefits with minor trade-offs in correctness, making it a suitable optimization strategy for resource-constrained environments. This study also demonstrates the feasibility of using SSM to synthesize findings from data strategy-based research.

Artificial Intelligence (AI) systems are technologies impacting our lives. The systems learn from existing datasets that record past human decisions. Their performance is measured in terms of accuracy, precision, and recall for reproducing already-known results. Understanding the system’s rationale is crucial to check for bias and accept such technology. Explainable AI (XAI) is the area devoted to opening the AI black box, and designing guidelines to build explainable AI systems. Nevertheless, it is important to understand the user’s needs for these explanations. This paper presents an investigation of the usefulness of XAI systems in the field of cancer diagnosis from the domain expert’s (oncologist) perspective. The main findings suggest domain experts (1) understood the outcomes of the XAI systems; (2) considered XAI outcomes as informative, rather than explanatory; (3) would like to go beyond the fixed presented perspective; and (4) missed the causal relation that would reveal the system’s rationale.

Background: research synthesis is still challenge in Software Engineering due to the heterogeneity of primary studies in the area. Also, it generates a significant volume of information which is complex to manage. Aims: to provide support to this kind of studies in SE. Method: we present the Evidence Factory, a tool designed to support the Structured Synthesis Method (SSM). SSM is a research synthesis method that can be used to aggregate both quantitative and qualitative studies. It is a kind of integrative synthesis method, such as meta-analysis, but has several features from interpretative methods, such as meta-ethnography, particularly those concerned with conceptual development. Results: the tool is a web-based infrastructure, which supports the organization of synthesis studies. Researchers can compare findings from different studies by modeling their results according to the evidence meta-model. After deciding whether the evidence can be combined, the tool automatically computes the uncertainty associated with the aggregated results using the formalisms from the Mathematical Theory of Evidence. Conclusion: the tool was used in real synthesis studies and is freely available for the SE community.

Context: Several empirical studies investigated the benefits and drawbacks of acquiring a Software Reference Architecture (SRA) to construct a family of software systems with similar architectural needs. However, these empirical results have not been synthesized by any study yet. Such synthesized evidence is essential to make informed decisions whether or not to adopt an SRA in an organization. Goal: To aggregate existing empirically- grounded evidence about the benefits and drawbacks of SRAs, aiming at supporting organizations' decision making on their adoption. Method: To identify primary studies in the technical literature through a systematic literature review, and then, use the Structured Synthesis Method (SSM) to aggregate qualitative and quantitative evidence through the use of diagrammatic models. Results: From the five identified primary studies, five SRA benefits have considerably increased their belief value after aggregation: interoperability of software systems, reduced development costs, improved communication among stakeholders, reduced risk, and reduced time- to-market. Also, one drawback of SRAs has increased its belief value: the required learning curve for developers. Conclusions: The aggregated results consolidate knowledge and confidence on some of the studied SRA effects. The commonly reported effects showed a clear increment of their belief and pointed out to broader generalization. The effects that did not show any belief increment are important to detect areas requiring further evidence to reach a higher degree of consolidation. Practitioners might benefit from these results to support the decision of adopting an SRA in practice.

This paper presents a literature survey of action research (AR) studies published in nine major Software Engineering (SE) journals and three conference proceedings in the period 1993 to June 2009. A strict selection based on distinguishing SE from Information Systems research has identified 16 papers. Although they represent a very small fraction of the studies being conducted in SE, such papers concern with different SE contexts allowing to get information about the increasing tendency in the AR use in software engineering. However, as shown by the initial results, SE researchers should invest more on rigor when defining, applying and reporting AR studies inSE.

Experimental studies have been used as a mechanism to acquire knowledge through a scientific approach based on measurement of phenomena in different areas. However it is hard to run such studies when they require models (simulation), produce large amount of information, and explore science in large scale. In this case, a computerized infrastructure is necessary and constitutes a complex system to be built. In this paper we discuss an experimentation environment that has being built to support large scale experimentation and scientific knowledge management in Software Engineering.