We apply a mixed research method to improve the user stories estimation process in a German company following agile software development. We combine software project data analytics with elicitation of teams' feedback, identify root causes for wrong estimates and propose an improved version of the estimation process. Three major changes are adopted in the new process: a shorter non numerical scale for story points, an analogy-based estimation process, and retrospectives analyses on the accuracy of previous sprints estimates. The new estimation process is applied on a new project, and an improvement of estimates accuracy from 10% to 45% is observed.

Combining Data Analytics with Team Feedback to Improve the Estimation Process in Agile Software Development / Vetro', Antonio; Dürre, Rupert; Conoscenti, Marco; Méndez Fernández, Daniel; Jørgensen, Magne. - In: FOUNDATIONS OF COMPUTING AND DECISION SCIENCE. - ISSN 0867-6356. - STAMPA. - 43:4(2018), pp. 305-334. [10.1515/fcds-2018-0016]

Combining Data Analytics with Team Feedback to Improve the Estimation Process in Agile Software Development

Antonio Vetrò;Marco Conoscenti;
2018

Abstract

We apply a mixed research method to improve the user stories estimation process in a German company following agile software development. We combine software project data analytics with elicitation of teams' feedback, identify root causes for wrong estimates and propose an improved version of the estimation process. Three major changes are adopted in the new process: a shorter non numerical scale for story points, an analogy-based estimation process, and retrospectives analyses on the accuracy of previous sprints estimates. The new estimation process is applied on a new project, and an improvement of estimates accuracy from 10% to 45% is observed.
File in questo prodotto:
File Dimensione Formato  
2018-fcds-ffc.pdf

accesso aperto

Descrizione: fcds ffc articolo open access
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 1.49 MB
Formato Adobe PDF
1.49 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2722515