Research
We bring the scientific method to software engineering: we design experiments and families of replications, analyse observational data causally and turn the results into evidence that practitioners can use.
What we study
Research lines
- 01.
Experimentation methodology, replication and evidence synthesis
We develop and evaluate methods to design, analyse, report and replicate software engineering experiments, including families of experiments, crossover designs and meta-analysis. We also study what makes evidence unreliable, such as statistical errors, publication bias and researcher bias.
- 02.
Causal inference and observational studies in mining software repositories
We adapt study designs from epidemiology, such as retrospective and prospective cohort studies and matching, to draw causal conclusions from software repository data. Recent studies examine how microservice adoption, code smells and static-analysis configurations affect development outcomes.
- 03.
Test-driven development and software testing
We run families of experiments in universities and companies to measure how test-driven development and testing techniques affect quality and productivity. We also examine how developers perceive testing effectiveness and how measurement instruments influence experimental results.
- 04.
Requirements engineering and human factors
We study empirically how the experience, domain knowledge and cognitive abilities of analysts shape requirements elicitation and inspection. We also investigate cognitive biases in software engineering.
- 05.
Usability and human-computer interaction
We investigate how to build usability into software development, from usability mechanisms and patterns to the evaluation of chatbots and collaborative modelling tools. In digital health, we design and evaluate mobile applications for older adults.
- 06.
Agile, rapid and continuous software engineering
We study how companies measure and manage quality, especially quality requirements, in agile and rapid software development. This work combines case studies and design science with industrial partners.
- 07.
Artificial intelligence and software engineering
We assess the methodological rigour of experiments that apply deep learning to software engineering tasks and build evidence-based AI tools. We also explore combining large language models with domain-specific languages for web engineering and teaching.
Funding
Research projects
Projects led by GRISE
Research projects whose principal investigator is, or was at the time, a GRISE member.
Ongoing
Completed
Análisis de la Calidad de los Estudios Experimentales en Minería de Repositorios Software
PID2022-137846NB-I00 · 2023–2026Mejora de la Calidad de los Experimentos en Ingeniería de Software para el Incremento de la Fiabilidad en los Resultados
PGC2018-097265-B-I00 · 2019–2022Mobility and Active Aging
EITH19669 · 2019Frailty, Falls, and Functional Loss Education
EITH17170 · 2017Operational Excellence in Industrial Software and Application Development with Empirical Software Engineering-phase 2
P1710470299 · 2016–2023Realizando experimentos en la industria del software: comprensión del paso de laboratorio a la realidad
TIN2014-60490-P · 2015–2017ESEIL — Empirical Software Engineering Industry Lab
2013–2017Tecnologías para la replicación y síntesis de experimentos en IS
TIN2011-23216 · 2012–2015Aggregation of Experiments Using Meta-Analysis
CCG10-UPM/TIC-5694 · 2010–2011RED MUIN (Mujeres en Informática)
CSO2008-05097-E · 2009–2012A framework for improving technology adoption decision-making
HD2008-0048 · 2009–2010Generación de Evidencias mediante Combinación de Resultados Experimentales
TIN2008-00555 · 2009–2011Tratamiento de Mecanismos de Usabilidad en las Etapas de Requisitos y Diseño de Software
TIC2005-00176 · 2005–2008Herramienta para la selección sistemática y formalizada de técnicas de prueba
2003–2004Definición de un Proceso Software integrado con técnicas de usabilidad
TIC-2002-00320 · 2002–2005Definición de un modelo conceptual polivalente para sistemas software desarrollados bajo la Ingenieria del Software y del Conocimiento
PB98-0721 · 1999–2002Collaborations
Projects led by other groups or institutions in which GRISE members take part.
Completed
Ecosistema MOTIVA: perspectiva tecnológica y motivacional
PID2022-140214OB-C21 · 2023–2026Hub de Innovación Digital en Inteligencia Artificial y Robótica para los Objetivos de Desarrollo Sostenible
2021–2023Active UP — Active UP: Dimensión Tecnológica y Científica
PID2019-108408RB-C21 · 2020–2023Plataforma Científico-tecnológica para el Pronóstico, Diagnóstico y Seguimiento de la Enfermedad Covid19
REACT-COVIDTECH-CM-23466 · 2020–2022eMadrid — Investigación y desarrollo de tecnologías educativas en la Comunidad de Madrid
S2018/TCS-4307 · 2019–2023POSITIVE — Maintaining and improving the intrinsic capacity involving primary care and caregivers
EITH19091 · 2019–2021Integrated supportive services/products to promote FrAilty Care and wEll function
2016–2018REHABILITA: Tecnologías Disruptivas para la Rehabilitación del Futuro
CEN-2009-1043 · 2009–2012Rehabilitación Sostenible de Edificios
P100215531 · 2009–2013Earlier projects of current members
Projects in which current members took part before joining GRISE.
Completed
Q-Rapids — Quality-aware rapid software development
H2020 grant 732253 · 2016–2019VALUE — FiDiPro VALUE: improving decision-making related to software-intensive products and services development
2015–2017N4S — Need for Speed
2014Cloud Software Program
2010–2013VULCANO – Promoción del desarrollo de software libre en un entorno de calidad y confianza adaptando las metodologías, procesos y modelos de negocio y ultimas tecnologías
2008–2009Flexi — Flexible Global Product Development and Integration
ITEA 2 project 06022; TSI-020400-2009-66 · 2007–2009Definición de un modelo conceptual orientado al problema en Ingeniería de Software
1999–2000Open science
Datasets & replication packages
We share the materials and data of our studies so that others can verify, replicate and extend them.
Figshare
Zenodo
Investigating Causality in Software Repositories Using Cohort-Based Methods - Replication Package
Figshare
Figshare
Zenodo
Figshare
Data from TDD Experiment Series
Zenodo
Zenodo
Figshare
Survey on Experimental Software Engineering Process - Answers
Zenodo
Zenodo
Using the SOCIO Chatbot for UML Modelling: A Second Family of Experiments
Figshare
An Empirical Study to Evaluate the Impact of Mindfulness at a Software Company (Laboratory Package)
Zenodo
Using the SOCIO Chatbot for UML Modelling: A Family of Experiments
Figshare
Quality measurement in agile and rapid software development: a systematic mapping [Dataset]
Need For Sleep - Replication package
Figshare
Archive
Web appendices of our papers
Supplementary material cited in our publications (kept at its original address).
- How We Selected and Analyzed the Studies (sites/extras/1)
- Usability elicitation patterns (USEPs) (sites/extras/2)
- Usability Supporting Architectural Pattern System Status Feedback (sites/extras/3)
- Towards the Systematization of Requirements Elicitation Technique Selection (sites/extras/4)
- Comparative analysis of meta-analysis methods (sites/extras/5)
- Quantitative determination of the relationship between internal validity and bias in software engineering experiments - consequences for systematic literature reviews (sites/extras/6)
- Programming Patterns for usability functionalities (sites/extras/7)
- Apéndice Web: Determinación Preliminar de las Variables que Influyen en la Educción de Requisitos (sites/extras/8)
- Web appendix of the paper entitled: A procedure for selecting an appropriate meta-analysis method: A comparison of approaches (sites/extras/9)
- Descriptions of the Replications Typologies (sites/extras/10)
- Test cases and raw data for the paper *** Empirical Evaluation of the Effects of Experience on Code Quality and Programmer Productivity: An Exploratory Study *** (sites/extras/11)
- Effectiveness for Detecting Faults Within and Outside the Scope of Testing Techniques: An Independent Replication (sites/extras/12)
- Results Classification Problems ISERN 2012 (sites/extras/14)
- Raw data (sites/extras/15)
- Hypothesis Testing in Experimental Software Engineering - ESEM'17 (sites/extras/18)
- Additional material for the paper 'Integrated Framework for Mobile Application Development' (sites/extras/20)
- Paper: Effectiveness for Detecting Faults Within and Outside the Scope of Testing Techniques: A Controlled Experiment (sites/trs/1)