Martí Manzano

dblp:200/9598 · DBLP profile ↗
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4ranked-venue papers
2as first author
3since 2021 · last 2024
0000-0001-7944-7232ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 3 · 1 first-author · 3 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
YearPublicationVenuePosition
2024 Forecasting software indicators: an industry-academia collaboration
abstract
Abstract Context Nowadays software-development organizations are urged to exploit their data for empowering their decision-making processes. Such data may be used to monitor the status of meaningful software indicators (e.g., software quality, productivity and on-time delivery) that are relevant for their decision-making processes. Forecasting the values of such indicators may provide evidence of a potentially high risk or opportunity that could help to anticipate actions accordingly. Most of the existing forecasting proposals in software engineering use open-source data rather than data from industrial projects. Therefore, there is a lack of evidence on how these proposals fit the particular needs of a software-development organization and how they can be automated into the organization’s infrastructure. Objective To enable software indicators´ forecasting in a software-development organization (Modeliosoft). Method We designed an industry-academia collaboration based on Action Design Research (ADR) to address Modeliosoft’s forecasting challenges. Results A tool-supported method called FOSI (Forecasting Of Software Indicators) for enabling forecasting in Modeliosoft. We obtained positive results regarding its suitability and technical feasibility in a pilot project of the organization. In addition, we provide details and reflections on the potential usefulness of the method for addressing similar field problems. Conclusions The procedures and results detailed in this paper are valuable to: 1) address Modeliosoft’s forecasting challenges 2) inspire other software-development organizations on how to deal with similar problems and even reuse some procedures and software support tools resulted from this work, 3) promote the win-win benefits of industry-academia collaborations.
Claudia P. Ayala, Cristina Gómez 0001, Martí Manzano, Antonin Abherve, Xavier Franch
Empir. Softw. Eng.3
2021 A Method to Estimate Software Strategic Indicators in Software Development: An Industrial Application
Martí Manzano, Claudia P. Ayala, Cristina Gómez 0001, Antonin Abherve, Xavier Franch, Emilia Mendes
Inf. Softw. Technol.1
2021 QaSD: A Quality-aware Strategic Dashboard for supporting decision makers in Agile Software Development
Lidia López 0001, Martí Manzano, Cristina Gómez 0001, Marc Oriol, Carles Farré, Xavier Franch, Silverio Martínez-Fernández, Anna Maria Vollmer
Sci. Comput. Program.2
2017 Combination of EEG Data Time and Frequency Representations in Deep Networks for Sleep Stage Classification
Martí Manzano, Alberto Guillén, Ignacio Rojas, Luis Javier Herrera
ICIC (2)1