The Evaluation of Open Source Software Innovativeness
- URL: http://arxiv.org/abs/2505.03855v1
- Date: Tue, 06 May 2025 07:53:11 GMT
- Title: The Evaluation of Open Source Software Innovativeness
- Authors: Nordine Benkeltoum,
- Abstract summary: It suggests an innovation typology supported by the notion of functional added value.<n>By showing the shortcomings of widely used innovation metrics, this research supports a new approach of innovativeness assessment specialized in each sector.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Product innovation assessment in software sector is a timely topic. Nevertheless, research on that subject is particularly scant. As a result, there is a lack of criteria to measure software innovativeness. In a context of theoretical and practical controversy in the open source field, this article assesses open source software innovativeness. Based on almost 500 cases studies and with the collaboration of 125 experts from industry, services and research fields, it suggests an innovation typology supported by the notion of functional added value. It provides also an innovation modelling framework that combines main evaluation methodologies. By showing the shortcomings of widely used innovation metrics, this research supports a new approach of innovativeness assessment specialized in each sector.
Related papers
- Measuring Software Innovation with Open Source Software Development Data [0.0]
This paper introduces a novel measure of software innovation based on open source software (OSS) development activity on GitHub.<n>We examine the dependency growth and release complexity among 350,000 unique releases from 33,000 unique packages across the JavaScript, Python, and Ruby ecosystems over two years post-release.
arXiv Detail & Related papers (2024-11-07T19:11:32Z) - Contemporary Software Modernization: Perspectives and Challenges to Deal with Legacy Systems [48.33168695898682]
"Software modernization" emerged as a research topic in the early 2000s.
Despite the large amount of work available in the literature, there are significant limitations.
arXiv Detail & Related papers (2024-07-04T15:49:52Z) - Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition [70.60872754129832]
First NeurIPS competition on unlearning sought to stimulate the development of novel algorithms.
Nearly 1,200 teams from across the world participated.
We analyze top solutions and delve into discussions on benchmarking unlearning.
arXiv Detail & Related papers (2024-06-13T12:58:00Z) - Application-Driven Innovation in Machine Learning [56.85396167616353]
We describe the paradigm of application-driven research in machine learning.
We show how this approach can productively synergize with methods-driven work.
Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation.
arXiv Detail & Related papers (2024-03-26T04:59:27Z) - NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems [50.076028127394366]
We present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems.<n>NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia.
arXiv Detail & Related papers (2023-04-10T15:12:09Z) - A new perspective on the prediction of the innovation performance: A
data driven methodology to identify innovation indicators through a
comparative study of Boston's neighborhoods [0.0]
The study uses a large geographically distributed dataset across Boston's 35 zip code areas.
In order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations.
arXiv Detail & Related papers (2023-04-04T05:45:50Z) - GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision.
We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
arXiv Detail & Related papers (2023-02-16T16:45:36Z) - Knowledge-enhanced Neural Machine Reasoning: A Review [67.51157900655207]
We introduce a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories.
We elucidate the current application domains and provide insight into promising prospects for future research.
arXiv Detail & Related papers (2023-02-04T04:54:30Z) - A New Innovation Concept on End user Contextual and Behavioural
Perspectives [0.0]
The phenomenon of innovation has been shifting away from focusing on tangible to intangible modernization with its vitalizing context.
This study proposes a new concept that will act as an overarching descriptor of innovation types.
arXiv Detail & Related papers (2021-10-09T10:45:10Z) - Closed-Form, Provable, and Robust PCA via Leverage Statistics and
Innovation Search [25.229137979402584]
We study the Innovation Values computed by the Innovation Search algorithm under a quadratic cost function.
It is proved that Innovation Values with the new cost function are equivalent to Leverage Scores.
This interesting connection is utilized to establish several theoretical guarantees for a Leverage Score based robust PCA method.
arXiv Detail & Related papers (2021-06-23T06:36:36Z) - Understanding Diffusion of Recurrent Innovations [0.0]
We present the first large-scale analysis of the adoption of recurrent innovations in the context of mobile app updates.
Our analysis reveals the adoption behavior and new adopter categories of recurrent innovations as well as the features that have impact on the process of adoption.
arXiv Detail & Related papers (2021-01-13T14:27:09Z) - Modeling, Visualization, and Analysis of African Innovation Performance [0.0]
We discuss the concepts and emergence of Innovation Performance, and how to quantify it, primarily working with data from the Global Innovation Index.
We briefly overview existing literature on using machine learning for modeling innovation performance, and use simple machine learning techniques, to analyze and predict the "Mobile App Creation Indicator" from the Global Innovation Index.
arXiv Detail & Related papers (2020-08-18T12:16:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.