Dataset - Understanding Product Discovery in Software Engineering: A Systematic Mapping Study

Abstract

This spreadsheet serves as the primary data repository and artifacts log for the execution of a Systematic Mapping Study (SMS) on Software Product Discovery, developed for the accompanying paper submitted to the Brazilian Symposium on Software Quality (SBQS). The dataset documents the end-to-end execution of the systematic mapping protocol, spanning search string validation using control papers, initial search retrieval across major digital repositories, multi-stage study selection and filtering, and in-depth qualitative and quantitative data extraction mapped directly to the paper's Research Questions (RQs / Questões de Pesquisa - QPs). Spreadsheet Architecture and Tab Breakdown The workbook is organized into three main dedicated worksheets (tabs) reflecting the sequential stages of the systematic mapping methodology: 1. Control Studies (Estudos de controle) Objective: Calibration and validation of search strings to ensure optimal search sensitivity and coverage before executing queries across primary digital databases. Contents: Benchmark literature on Software Product Discovery, Software Product Management, and Dual-Track Agile published between 2011 and 2025 across major venues (Springer, ScienceDirect, IEEE, Scopus). Key metadata including ID, source database, publication year, authors, title, and methodological relevance analysis. Ensures that foundational literature (e.g., PDISC method, Dual-Track Agile education, DEEP maturity model, and grey literature reviews) is successfully captured by the defined search string. 2. Primary Study Screening & Selection Pipeline (Todos os Artigos) Objective: Registration and tracking of all candidate studies retrieved from electronic databases through the screening pipeline. Dataset Scale: Contains 715 individual study records collected across four major digital libraries: Springer: 403 papers ACM Digital Library: 249 papers IEEE Xplore: 38 papers Scopus: 25 papers Pipeline Tracking: Tracks study progression across three screening stages (1ª Etapa, 2ª Etapa, 3ª Etapa). Documents specific inclusion and exclusion criteria applied during title, abstract, and full-text reading. Details rejection justifications (e.g., duplicate records across databases, non-English/Portuguese languages, unavailable full texts, traditional software engineering/requirements engineering without product discovery focus, or technical domain misalignment such as web service discovery or machine learning models). Tracks final selection outcomes, identifying 25 candidate studies approved during screening. 3. Data Extraction Matrix (Extração) Objective: Comprehensive data extraction and thematic synthesis for the 17 primary studies selected for final inclusion and synthesis in the systematic mapping paper. Extraction Fields: Bibliographic Data: ID, full BibTeX citation, inline citation keys, study title, and publication year. Contextual Information: Executive abstract summary, organizational/research context, and primary research objectives. Research Question Synthesis (QPs): [QP1] Frameworks, Models, and Processes: Identification of structured methodologies and processes (e.g., PDISC model, Dual-Track Agile, Lean UX, Continuous Discovery, and DEEP maturity model). [QP2] Techniques, Research, and Validation: Catalog of empirical techniques and validation mechanisms used during product discovery (e.g., interviews, prototyping, Design Studio, story mapping, A/B testing, Wizard of Oz, and user testing). [QP3] Metrics and Key Performance Indicators: Quantitative and qualitative measurement instruments (e.g., DEEP Score, Discovery Effort Worthiness Index, user engagement KPIs, and usability heuristics). [QP4] Gaps, Challenges, and Limitations: Multi-perspective categorization (qualitative text, summary categories, and specific items) covering operational, strategic, cultural, resource, and governance challenges faced in industrial product discovery adoption. Methodological Rigor and Transparency By systematically archiving each evaluation step—from initial retrieval and exclusion rationale to fine-grained taxonomy extraction—this spreadsheet ensures auditability, transparency, and full reproducibility of the Systematic Mapping Study in accordance with established Software Engineering literature synthesis guidelines.