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Early Value Argumentation and Prediction: An Iterative Approach to Quantifying Feature Value
Malmö högskola, Faculty of Technology and Society (TS). Malmö högskola, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-4908-2708
Malmö högskola, Faculty of Technology and Society (TS). Malmö högskola, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-7700-1816
2015 (English)In: Product-Focused Software Process Improvement, Springer, 2015, p. 16-23Conference paper, Published paper (Refereed)
Abstract [en]

Companies are continuously improving their practices and ways of working in order to fulfill always-changing market requirements. As an example of building a better understanding of their customers, organizations are collecting user feedback and trying to direct their R&D efforts by e.g. continuing to develop features that deliver value to the customer. We (1) develop an actionable technique that practitioners in organizations can use to validate feature value early in the development cycle, (2) validate if and when the expected value reflects on the customers, (3) know when to stop developing it, and (4) identity unexpected business value early during development and redirect R&D effort to capture this value. The technique has been validated in three experiments in two cases companies. Our findings show that predicting value for features under development helps product management in large organizations to correctly re-prioritize R&D investments.

Place, publisher, year, edition, pages
Springer, 2015. p. 16-23
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9459
Keywords [en]
Continuous experimentation, EVAP, QCD, Data-driven development, Customer-driven development
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-12633DOI: 10.1007/978-3-319-26844-6_2ISI: 000367570400005Scopus ID: 2-s2.0-84952326992Local ID: 19790OAI: oai:DiVA.org:mau-12633DiVA, id: diva2:1409680
Conference
16th International Conference, PROFES, Bolzano, Italy (2015)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2024-04-04Bibliographically approved
In thesis
1. Developing the right features: the role and impact of customer and product data in software product development
Open this publication in new window or tab >>Developing the right features: the role and impact of customer and product data in software product development
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Software product development companies are increasingly striving to become data-driven. The access to customer feedback and product data has been, with products increasingly becoming connected to the Internet, demonetized. Systematically collecting the feedback and efficiently using it in product development, however, are challenges that large-scale software development companies face today when being faced by large amounts of available data. In this thesis, we explore the collection, use and impact of customer feedback on software product development. We base our work on a 2-year longitudinal multiple-case study research with case companies in the software-intensive domain, and complement it with a systematic review of the literature. In our work, we identify and confirm that large-software companies today collect vast amounts of feedback data, however, struggle to effectively use it. And due to this situation, there is a risk of prioritizing the development of features that may not deliver value to customers. Our contribution to this problem is threefold. First, we present a comprehensive and systematic review of activities and techniques used to collect customer feedback and product data in software product development. Next, we show that the impact of customer feedback evolves over time, but due to the lack of sharing of the collected data, companies do not fully benefit from this feedback. Finally, we provide an improvement framework for practitioners and researchers to use the collected feedback data in order to differentiate between different feature types and to model feature value during the lifecycle. With our contributions, we aim to bring software companies one step closer to data-driven decision making in software product development.

Place, publisher, year, edition, pages
Malmö university, Faculty of Technology and Society, 2016. p. 243
Series
Studies in Computer Science ; 3
Keywords
customer feedback, data-driven development, feature value, feature differentiation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-7794 (URN)21268 (Local ID)978-91-7104-736-6 (ISBN)978-91-7104-737-3 (ISBN)21268 (Archive number)21268 (OAI)
Presentation
2016-11-11, Niagara, NI:B0E07, Malmö, 13:15 (English)
Opponent
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2024-04-04Bibliographically approved

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fulltext(1521 kB)237 downloads
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Publisher's full textScopushttp://profes2015.inf.unibz.it/

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Fabijan, AleksanderOlsson Holmström, Helena

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