Dataset. 2021
Quality measurement in agile and rapid software development: a systematic mapping [Dataset]
UPCommons. Portal del coneixement obert de la UPC
oai:upcommons.upc.edu:2117/335910
UPCommons. Portal del coneixement obert de la UPC
- López Cuesta, Lidia|||0000-0002-6901-9223
- Burgués Illa, Xavier|||0000-0001-6974-9886
- Martínez Fernández, Silverio Juan|||0000-0001-9928-133X
- Vollmer, Anna Maria
- Behutiye, Woubshet
- Karhapää, Pertti
- Franch Gutiérrez, Javier|||0000-0001-9733-8830
- Rodriguez, Pilar
- Oivo, Markku
The dataset contains the following files: (1) readme.txt, it is a text file including details about the content of the dataset; (2) Measurement in ARSD_SMS_Data.xls, it is a MS Excel file containing the data used in the analysis of the primary studies, reported in Section 4 of the paper; and, (3) Metrics in ARSD.pdf, it is pdf file containing the complete list of metrics reported in the different primary studies. The metrics are classified into the following categories: QR metrics, QMIs, and QMI metrics. In the MS Excel file, the data is organised in separated sheets: (a) RQ1 - Literature characterisation; (b) RQ2 - QR Metrics, where QR refers to Quality Requirements; (c) RQ3 - QMI, where QMI refers to Quality Management Indicators; (d) RQ3 - Tools, corresponding to the reported tools used to manage QMIs; and, (e) RQ3 - QMI Metrics., This dataset has been used to generate part of the results included in the manuscript: “Quality Measurement in Agile and Rapid Software Development: A Systematic Mapping”, submitted to the Information and Software Technology journal (IST) at the time of publishing this dataset. The paper reports the results of a systematic mapping study surveying the literature related to quality requirements management through metrics in agile and rapid software development processes.
The dataset contains the data extracted from the primary studies that was used in the analysis phase of the study and reported in the results section of the paper. The data extracted from the primary studies has been complemented with some data used for specific purposes, e.g. during the analysis phase, we included the list of companies collaborating in the paper analysing the authors’ affiliations to understand the level of implication of companies in the field.
Complementing the data analysed, the dataset includes some detailed results that are not included in the body of the paper, i.e., the list of metrics reported in the selected primary studies.
DOI: http://hdl.handle.net/2117/335910, https://dx.doi.org/10.5821/data-2117-335910-1
UPCommons. Portal del coneixement obert de la UPC
oai:upcommons.upc.edu:2117/335910
HANDLE: http://hdl.handle.net/2117/335910, https://dx.doi.org/10.5821/data-2117-335910-1
UPCommons. Portal del coneixement obert de la UPC
oai:upcommons.upc.edu:2117/335910
Ver en: http://hdl.handle.net/2117/335910, https://dx.doi.org/10.5821/data-2117-335910-1
UPCommons. Portal del coneixement obert de la UPC
oai:upcommons.upc.edu:2117/335910
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1 Documentos relacionados
1 Documentos relacionados
UPCommons. Portal del coneixement obert de la UPC
oai:upcommons.upc.edu:2117/359546
Artículo científico (article). 2022
QUALITY MEASUREMENT IN AGILE AND RAPID SOFTWARE DEVELOPMENT: A SYSTEMATIC MAPPING
UPCommons. Portal del coneixement obert de la UPC
- López Cuesta, Lidia|||0000-0002-6901-9223
- Burgués Illa, Xavier|||0000-0001-6974-9886
- Martínez Fernández, Silverio Juan|||0000-0001-9928-133X
- Vollmer, Anna Maria
- Behutiye, Woubshet
- Karhapää, Pertti
- Franch Gutiérrez, Javier|||0000-0001-9733-8830
- Rodríguez, Pilar
- Oivo, Markku
Context: In despite of agile and rapid software development (ARSD) being researched and applied extensively, managing quality requirements (QRs) are still challenging. As ARSD processes produce a large amount of data, measurement has become a strategy to facilitate QR management. Objective: This study aims to survey the literature related to QR management through metrics in ARSD, focusing on: bibliometrics, QR metrics, and quality-related indicators used in quality management. Method: The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. Results: We selected 61 primary studies (2001-2019). Despite a large body of knowledge and standards, there is no consensus regarding QR measurement. Terminology is varying as are the measuring models. However, seemingly different measurement models do contain similarities. Conclusion: The industrial relevance of the primary studies shows that practitioners have a need to improve quality measurement. Our collection of measures and data sources can serve as a starting point for practitioners to include quality measurement into their decision-making processes. Researchers could benefit from the identified similarities to start building a common framework for quality measurement. In addition, this could help researchers identify what quality aspects need more focus, e.g., security and usability with few metrics reported., This work has been funded by the European Union’s Horizon 2020 research and innovation program through the Q-Rapids project (grant no. 732253). This research was also partially supported by the Spanish Ministerio de Economía, Industria y Competitividad through the DOGO4ML project (grant PID2020-117191RB-I00). Silverio Martínez-Fernández worked in Fraunhofer IESE before January 2020., Peer Reviewed
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