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.

The evidence-based software engineering approach advocates the use of scientific evidence by software engineers to support the adoption of software technologies in industrial software development and maintenance projects. Aside from the unavailability of scientific knowledge in industrial settings and the time required to acquire evidence in the software engineering (SE) field, additional challenges prevent practitioners, mainly those that are not experienced in research, from collecting knowledge from scientific sources to support the decisionmaking throughout their software projects.

The Statistical Process Control (SPC) has been adopted by software development organizations to improve process management and assessment. Without appropriate support tools and a process model to guide the organizations during to SPC, such practice is costly, considering the need to collect and analyze information from several process enactments. To aid in the SPC implementation, this paper proposes a standard process model and an approach for quantitative analysis of projects that was integrated to a Process-centered Software Engineering Environment.